Podcasts about cohere

Hoy hablamos de Cohere liberando Command A+ con licencia Apache 2.0 y empujando la IA soberana de verdad; de la task force del Pentágono para meter IA con capacidades ofensivas en NSA y Cyber Command; del plan de xAI para comprar 2.800 millones en turbinas de gas mientras acumula demandas ambientales; de Jensen Huang admitiendo que Nvidia ha cedido gran parte del mercado chino a Huawei; y de los 81.600 millones trimestrales con los que Nvidia confirma que, en la fiebre de la IA, el gran negocio sigue siendo vender palas.Puedes seguirnos en YouTube en https://youtube.com/olivernabani y puedes unirte al Discord Mashain en https://olivernabani.com/discord

Cohere is making a bold move into biotech with the acquisition of Montreal's Reliant AI, aiming to bring agent-powered tools to drugmakers. Plus, Canada's army is planning a major reorganization that could add new divisions, drones, and long-range firepower. In The Big Picture, we break down the latest inflation numbers, Nissan's reported plans to ship Chinese-made EVs into Canada, and Google's shift toward a chatbot-style search experience.The Peak Daily is produced in partnership with reframevid.com

Send us Fan MailMatt Fitzpatrick is the CEO of Invisible Technologies, an AI platform used to improve models for more than 80% of the world's leading AI companies, including Microsoft, AWS, and Cohere. The company has raised $100 million and scaled to $134 million in revenue, making it one of the fastest-growing AI companies globally.Before joining Invisible, Matt was the Global Head of QuantumBlack Labs at McKinsey, where he led large-scale AI and data engineering efforts and helped enterprises move from experimentation to production.In this episode, Matt draws on years spent inside enterprise AI deployments to challenge the gap between model progress and real-world adoption, and to explain why most organizations still struggle to turn AI into measurable business outcomes.In this conversation, we discuss:Why enterprise AI adoption lags far behind model performance improvements, and why most organizations still struggle to turn technical progress into real business impactThe hidden role of messy, fragmented legacy data, and why decades of accumulated systems make it nearly impossible to deploy reliable AI at scaleWhy defining “good” output in generative AI is far harder than expected, and how unclear standards stall deployment across high-stakes enterprise workflowsThe case for redesigning workflows from scratch, and why layering AI on top of existing processes fails to create meaningful efficiency gainsWhy most AI initiatives fail due to lack of business ownership, and how separating technology teams from operators prevents projects from reaching productionHow fear-driven narratives about job loss are slowing adoption, and why AI is more likely to shift work toward higher-value tasks than eliminate roles entirely Explore this conversation: 00:00 Intro and Fun Fact 03:57 Matt Fitzpatrick's Path From McKinsey to Invisible Technologies 09:56 Scaling Enterprise AI with Modular Platforms and Clean Data 12:44 The Crucial Role of Expert Human Feedback in Model Training 17:56 Why 95% of Enterprise AI Projects Never Reach Production21:38 The Missing Link: Why True AI Transformation Requires Business Ownership 26:54 Overcoming AI Fear and the Reality of Jevons Paradox 32:24 Responsible AI: Governing Outcomes Over Technology 39:05 The Future of Work: Moving From Administration to Innovation 44:12 Where to Connect with Matt Fitzpatrick and Invisible TechnologiesResources:Subscribe to the AI & The Future of Work NewsletterConnect with Matthew on LinkedInAI fun fact articleOn How Allison Baum Gates Reveals the Secrets to a Successful VC Career

Meta veut resserrer ses mesures de vérification de l’âge et mise sur l’IA pour empêcher les jeunes de 13 ans ou moins d’accéder à ses plateformes, dont Instagram. Est-ce que ça vous rassure? Pascal et Alain en discutent, et font le pari que la vérification d’âge va devenir beaucoup plus stricte à l’avenir. Aussi: L’IA canadienne Cohere a vu sa valeur bondir de 7 à 20 milliards d’un coup. C’est la plus importante entreprise d’IA au monde qui n’est pas américaine ou chinoise! Est-ce une bonne nouvelle? Promo InfoBref: pour l'essentiel des nouvelles en 5 minutes, inscrivez vous à https://infobref.com/utdt Dans cet épisode: Anthropic va chez xAI pour son superordinateur Colossus Google Health et Fitbit Air: la santé connectée réorganisée reMarkable lance une Paper Pure meilleur marché On a moins de mots passe qu’il y a 5 ans La 6G assurera la souveraineté numérique Promo PlanetHoster: La souveraineté de vos données vous inquiète? La solution Code promo : PHA-UTDT The World N0C - Hébergement mutualisé - https://bit.ly/phutdtm HybridCloud N0C - Hébergement dédié - https://bit.ly/phutdt Testés: La guitare Aeroband vous apprendra à maîtriser les accords Sinopé Calypso V2: un chauffe-eau connecté québécois qui déjoue Hilo Et plus! Voir https://www.cogecomedia.com/vie-privee pour notre politique de vie privée

Cohere übernimmt Aleph Alpha – die Schwarz-Gruppe investiert $600 Mio., Aleph Alpha bekommt 10% der neuen Firma. OpenAI und Microsoft lösen ihre exklusive Partnerschaft auf – Microsoft verliert die Exklusivität, OpenAI behält den Revenue Share. OpenAI verfehlt interne Umsatz- und Nutzerziele. Google investiert bis zu $40 Mrd. in Anthropic – die ersten $10 Mrd. auf der alten $350-Mrd.-Bewertung, obwohl Anthropic am Sekundärmarkt über $1 Billion wert ist. Google unterzeichnet einen geheimen Pentagon-KI-Deal trotz Mitarbeiterprotesten. GitHub Copilot wechselt auf nutzungsbasierte Abrechnung. Ex-DeepMind-Forscher raised $1 Mrd. Seed auf $5 Mrd. Bewertung. World ID 4.0 startet mit Zoom, Tinder und Shopify. China blockiert Metas $2-Mrd.-Manus-Übernahme. Emil Michael baut Pentagon-VC-Fonds. Musk vs. Altman geht vor Gericht – Musk pusht den New-Yorker-Artikel, NYT enthüllt SpaceX als Musks Sparkasse. Sereact raised $110 Mio. für Robotik-KI. Google Maps zeigt jetzt die Zahl gelöschter Rezensionen an. Unterstütze unseren Podcast und entdecke die Angebote unserer Werbepartner auf ⁠⁠⁠⁠⁠⁠doppelgaenger.io/werbung⁠⁠⁠⁠⁠⁠. Vielen Dank!  Philipp Glöckler und Philipp Klöckner sprechen heute über: (00:00:00) Hörerfrage (00:07:20) Aleph Alpha/Cohere: Analyse des Deals (00:16:56) OpenAI/Microsoft lösen exklusive Partnerschaft (00:21:06) OpenAI verfehlt Umsatz- und Nutzerziele (00:28:40) Google investiert $40 Mrd. in Anthropic bei alter Bewertung (00:37:08) Google: Geheimer Pentagon-KI-Deal (00:41:15) Bubble: $5 Mrd. Seed, Cognition $25 Mrd. (00:45:51) World ID 4.0, China blockiert Meta/Manus (00:52:05) Mythos: Panik bei Firmen, Emil Michael als Pentagon-VC (00:55:55) Musk vs. Altman vor Gericht & Musk nutzte SpaceX als Sparkasse (01:02:40) Sereact $110 Mio. und Google Maps gelöschte Reviews Shownotes Cohere kauft Aleph Alpha, Schwarz investiert $600 Mio. - bloomberg.com OpenAI und Microsoft: Neue Freiheiten für beide Seiten - wsj.com OpenAI verfehlt Umsatz- und Nutzerziele vor IPO - wsj.com Google investiert bis zu $40 Mrd. in Anthropic - wsj.com Google unterzeichnet geheimen Pentagon-KI-Deal - theinformation.com GitHub Copilot wechselt auf nutzungsbasierte Abrechnung - github.blog Sequoia/Nvidia investieren $5 Mrd. in Ex-DeepMind-Startup - bloomberg.com Cognition (Devon AI) bei $25 Mrd. Bewertung - bloomberg.com World ID 4.0: Partnerschaften mit Zoom, Tinder, Shopify - xcancel.com China blockiert Metas $2 Mrd. Manus-Übernahme - theinformation.com Meta: Rechenzentren mit Solarenergie aus dem All - bloomberg.com Mythos- ft.com Emil Michael verwandelt Pentagon in VC-Firma - washingtonpost.com Musk pusht Altman-Exposé auf X vor Prozess - wired.com NYT: Musk nutzte SpaceX als Sparkasse - nytimes.com Sereact: $110 Mio. für Robotik-KI aus Stuttgart - bloomberg.com Google Maps zeigt Zahl gelöschter Rezensionen an - smartdroid.de

In this episode of Tank Talks, Matt Cohen and John Ruffolo break down one of the biggest economic policy announcements in Canada's innovation economy: Mark Carney's proposed $25 billion Canada Strong Fund, a sovereign wealth fund designed to invest in nation-building projects, strategic industries, Canadian technology companies, and long-term economic sovereignty. John, who previously argued for this type of fund in his Substack piece Canada's Missing Pot of Gold, explains why Canada's biggest structural problem is undercapitalization and why relying on foreign direct investment for critical industries creates serious sovereignty risks.Matt and John dig into the hard questions behind the fund: Where does the money come from? Can Canada borrow at low rates and invest for long-term returns? How should the fund be governed so it does not become a political slush fund? And can this vehicle finally force a more serious conversation around Canadian pension funds, domestic capital formation, and backing companies like Cohere, Kepler, and Xanadu before they are pushed toward foreign capital markets?The episode also covers Cohere's acquisition of German AI firm Aleph Alpha, the rise of sovereign AI alternatives outside the U.S. and China, Xanadu's volatile post-SPAC quantum stock run, SpaceX's reported Cursor acquisition talks, Meta's 8,000-person AI-driven workforce reduction, and Thoma Bravo's massive Medallia equity wipeout. From sovereign wealth and AI infrastructure to quantum financing and private equity pain, this episode asks the real question: can Canada build the capital systems needed to own its future?Canada Strong Fund: Carney's $25B sovereign wealth fund announcement (00:31)Matt opens the episode by laying out the breaking news: Mark Carney has launched the proposed Canada Strong Fund, a $25 billion sovereign wealth fund aimed at giving Canadians a stake in strategic national projects and critical industries.Why John Ruffolo says Canada is dangerously undercapitalized (01:22)John argues that Canada's core economic problem is not a lack of ideas, talent, or companies, but a lack of domestic capital formation. He explains why foreign-controlled capital in sovereign industries is a bad idea and why Canada needs its own funding mechanism.The biggest risk: governance or political slush fund? (03:14)John explains that the Canada Strong Fund will only work if it is independently governed, similar to CPPIB or CDPQ. Without strong governance, he warns, the fund could collapse into politically motivated pet projects.Can Canada borrow at 3.5% and earn 7% long term? (04:59)John breaks down the financial logic behind using Canada's strong credit rating to borrow at lower rates and invest through a professionally managed fund targeting long-term returns similar to major pension funds.Why the fund fails if returns do not materialize (08:15)Matt raises concerns about launching a sovereign wealth fund during a deficit environment. John says the idea only works if the fund is independently managed and capable of generating real long-term returns.No more grants: John's blunt plan for government funding (14:02)John calls for Canada to stop giving grants, especially to foreign-based companies, and instead convert government support into equity investments that create long-term ownership and capital recycling for the country.Cohere acquires Aleph Alpha and makes a sovereign AI play (16:12)Matt breaks down Cohere's acquisition of German AI firm Aleph Alpha, the new Berlin European headquarters, and the reported $600 million financing commitment from Schwarz Group as part of a broader sovereign AI strategy.Xanadu's quantum stock surge and post-SPAC volatility (19:59)Matt explains Xanadu's post-SPAC trading action, including its sharp rise, options activity, and SEC filing registering nearly 300 million Class B shares for sale after the lockup period expires.SpaceX, Cursor, and peak AI paper-deal froth (24:25)Matt and John react to reports that SpaceX could acquire AI coding startup Cursor for $60 billion, with John arguing that SpaceX shareholders should be furious about the growing complexity and governance concerns.Meta layoffs and the real cost of AI capital spending (27:56)Matt highlights Meta's reported 10% workforce reduction tied to massive AI capital spending. John argues the “AI efficiency” explanation often masks bad capital allocation and failed strategic bets.Thoma Bravo's $5.1B Medallia equity wipeout (29:55)The episode closes with Thoma Bravo handing Medallia back to creditors after a major private equity software deal collapses, raising questions about SaaS valuations, debt structures, and exit assumptions in the AI era.Connect with John Ruffolo on LinkedIn: https://ca.linkedin.com/in/joruffoloConnect with Matt Cohen on LinkedIn: https://ca.linkedin.com/in/matt-cohen1Visit the Ripple Ventures website: https://www.rippleventures.com/ This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit tanktalks.substack.com

Diese Woche wird improvisiert – und zwar hochkarätig: Während Dietmar Deffner entspannt durch Venedig gondelt, übernimmt Pip Klöckner das Mikro. Gemeinsam mit Holger Zschäpitz analysiert er den KI-Hype, den Machtkampf zwischen OpenAI, Microsoft und Anthropic – und erklärt, warum die nächsten Börsengänge alles verändern könnten und wieso schon zwei Stunden ChatGPT messbar Hirnareale schrumpfen lassen. Außerdem verrät Pip, welche KI-Modelle er privat abonniert hat, warum Jobs doch nicht so schnell verschwinden wie gedacht – und was hinter dem Milliarden-Spiel von SpaceX steckt. Dazu: Ein Vorgeschmack auf Pips mit Spannung erwartete OMR-Präsentation von der HBO-Bühne mit 120 Slides in 50 Minuten und das stille Begräbnis von Aleph Alpha bei Cohere. Plus: Holgers Lebensphilosophie zwischen DDR-Improvisation und der entscheidenden Frage seiner Bonner Tante – und Bulle/Bär zwischen Anthropic-Boom und Chinas Demografie-Schock. DEFFNER & ZSCHÄPITZ sind wie das wahre Leben. Wie Optimist und Pessimist. Im wöchentlichen WELT-Podcast diskutieren und streiten die Journalisten Dietmar Deffner und Holger Zschäpitz über die wichtigen Wirtschaftsthemen des Alltags. Schreiben Sie uns an: wirtschaftspodcast@welt.de Impressum: https://www.welt.de/services/article7893735/Impressum.html Datenschutzerklärung: https://www.welt.de/services/article157550705/Datenschutzerklaerung-WELT-DIGITAL.html

Jaeden sits down with Nick Frosst, co-founder of Cohere, to discuss the company's focus on enterprise AI, foundational models, and AI sovereignty. Nick shares why Cohere prioritizes practical and secure solutions over chasing AGI, and how businesses can avoid common mistakes when adopting AI technology.Watch on YouTube: https://youtu.be/Qk9kXX0erTAConor's AI Course: https://www.ai-mindset.ai/coursesGet the top 80+ AI Models for $8.99 at AI Box: https://aibox.aiCheck out Cohere: https://cohere.com/Chapters00:00 Introduction to Cohere and AI Background03:31 Cohere's Unique Approach to AI for Enterprises06:20 Real-World Applications of Cohere's Technology09:12 The Evolution of AI Models and Their Utility12:30 ROI vs AGI: A Pragmatic Approach to AI16:14 Concerns in the AI Industry and Sovereignty22:26 Capital Efficiency in AI Development27:57 Common Mistakes in AI Adoption by Enterprises30:28 The Future of Enterprise AI See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Jaeden sits down with Nick Frosst, co-founder of Cohere, to discuss the company's focus on enterprise AI, foundational models, and AI sovereignty. Nick shares why Cohere prioritizes practical and secure solutions over chasing AGI, and how businesses can avoid common mistakes when adopting AI technology.Watch on YouTube: https://youtu.be/Qk9kXX0erTAConor's AI Course: https://www.ai-mindset.ai/coursesGet the top 80+ AI Models for $8.99 at AI Box: https://aibox.aiCheck out Cohere: https://cohere.com/Chapters00:00 Introduction to Cohere and AI Background03:31 Cohere's Unique Approach to AI for Enterprises06:20 Real-World Applications of Cohere's Technology09:12 The Evolution of AI Models and Their Utility12:30 ROI vs AGI: A Pragmatic Approach to AI16:14 Concerns in the AI Industry and Sovereignty22:26 Capital Efficiency in AI Development27:57 Common Mistakes in AI Adoption by Enterprises30:28 The Future of Enterprise AI

Aktien hören ist gut. Aktien kaufen ist noch besser. Unser Partner Scalable Capital ist jetzt Bank und bietet euch dadurch jetzt noch bessere Konditionen. Mehr Infos findet ihr unter: scalable.capital/oaws. Iran-Verhandlungen gecancelt. Rüstungsaktien fallen neun Tage. Novo schlägt Lilly. X-Energy startet mit 10 Milliarden Börsenwert. Alphabet investiert in Anthropic. Aleph Alpha x Cohere. Canon & Jungheinrich leiden. SpaceX will KI. Procter & Gamble wird Influencer. Intel (WKN: 855681) legt 24% zu, stärkster Kursanstieg seit 1987. Die CPU-Nachfrage explodiert, Yields verbessern sich schneller als geplant. Trumps 9 Mrd. $ Investition ist jetzt 36 Mrd. $ wert. Brasilien boomt. Der MSCI Latinamerika liegt 20% im Plus. Petrobras (WKN: 541501) profitiert vom Ölpreis, friert aber Inlandspreise ein. Itaú Unibanco (WKN: A0RGKJ) lockt mit KGV 10 und 7% Dividende. Diesen Podcast vom 27.04.2026, 3:00 Uhr stellt dir die Podstars GmbH (Noah Leidinger) zur Verfügung. Learn more about your ad choices. Visit megaphone.fm/adchoices

Rajiv sits down with Neil Sheperd (formerly Cohere, Scale AI, BCG, McKinsey) and Amit Malhotra (formerly buybuy Baby, 1-800-Contacts) to get brutally specific about how humans and Agentic AI will coexist in the future workplace.  They discuss how AI changes tasks first and why the shock may hit high-skill jobs sooner than most people expect. We debate agent guardrails, attention economics in B2B marketing, and the leadership skills that still matter when execution gets automated.• AI replacing tasks before whole jobs• Why high-paid cognitive work can be disrupted fast• What makes agentic AI different from expert systems• Enterprise mistakes like boxing work into factory tasks• B2B marketing when content gets commoditized• Brand trust as a shortcut for scarce attention• Guardrails to prevent KPI chasing and hidden technical debt• Using tight use cases and human-in-the-loop verification• American Dream Index and AI as an inequality accelerant• Lessons from imperial governance for decentralized autonomy• How org charts tighten while individuals become “IC++” with agents• Clear intent-driven orders as the new management skillAI isn't waiting politely at the edges of the org chart. It's already taking tasks, and the uncomfortable twist is that high-skill, high-wage work may feel the impact sooner because the cost arbitrage is impossible to ignore. From Park City during our Growth Marketing Summit, we gather for a roundtable on the future of human work, enterprise adoption of AI, and what this acceleration means for leaders trying to stay useful and humane at the same time.We talk agentic AI beyond the demo: where it actually lands in real companies, why “optimize the KPI” can become a Trojan horse that piles up technical debt, and how to design guardrails when agents move faster than any human can audit. We also dig into B2B marketing strategy as the cost of content trends toward zero, making attention, trust, and brand credibility the real battleground for growth.Neil shares what he's building with the American Dream Index and why affordability data matters when AI can widen inequality. Amit brings lessons from past tech cycles plus a surprising angle from imperial history on decentralized governance, autonomy, and scope creep. We end with what leadership looks like in 2026: clear intent, better judgment, and teams that include both people and AI agents.Neil Shepherd: https://www.linkedin.com/in/neilshep/Neil Shepherd, Neil is the Founder of the American Dream Index.  A seasoned growth executive with 25 years of experience in Silicon Valley, he most recently served as the VP of Growth at Cohere, and has led marketing and digital strategy at organizations like BCG, ScaleAI, PayPal, and McKinsey. Neil is an expert in product-led growth, data science, and leveraging generative AI, where he helps companies scale their revenue and user acquisition.Amit Malhotra: https://www.linkedin.com/in/amitx/Amit Malhotra is a Private Equity Operating Advisor and technology builder. With over two decades of experience at the intersection of AI, digital transformation, and business growth, he has led massive turnarounds and rebuilt technology stacks from scratch for major brands like buybuyBABY and 1-800 Contacts.Website: https://www.position2.com/podcast/Rajiv Parikh: https://www.linkedin.com/in/rajivparikh/Sandeep Parikh: https://www.instagram.com/sandeepparikh/Email us with any feedback for the show: sparkofages.podcast@position2.com

OpenAI liefert eine Rekordwoche: ChatGPT Images 2 kombiniert Reasoning mit Bildgenerierung – Infografiken, Speisekarten und Comicstrips entstehen per Prompt. GPT 5.5 überholt Claude Opus 4.7 in vielen Benchmarks. SpaceX will Cursor für $60 Mrd. übernehmen, inklusive $10 Mrd. Breakup-Fee – vermutlich um KI-Revenue fürs IPO aufzupolieren. Der SpaceX-IPO-Prospekt taxiert den eigenen Markt auf $28,5 Billion, davon $26,5 Billion für KI. Amazon kauft den Telko-Anbieter Globalstar, Project Houdini beschleunigt modularen Data-Center-Bau. DeepSeek V4 erscheint als stärkstes chinesisches Open-Source-Modell. Anthropic kooperiert mit Freshfields für Legal AI. OpenAI holt Ex-Airbnb-Manager für EMEA. USVC will VC-Investments ab $500 für Kleinanleger öffnen. Polymarket führt Perpetuals ein. Meta, Microsoft und Snap entlassen Tausende. Tesla liefert solide Earnings, aber FSD funktioniert nicht auf Hardware 3 – eine Lüge an Käufer. Meta will Mausbewegungen und Tastaturanschläge der Mitarbeiter für KI-Training erfassen. Samsung-Mitarbeiter streiken. Talon One wird für €750 Mio. an Adyen verkauft. Aleph Alpha wird von Cohere übernommen.  Unterstütze unseren Podcast und entdecke die Angebote unserer Werbepartner auf ⁠⁠⁠⁠⁠⁠doppelgaenger.io/werbung⁠⁠⁠⁠⁠⁠. Vielen Dank!  Philipp Glöckler und Philipp Klöckner sprechen heute über: (00:00:00) ChatGPT Images 2 (00:07:19) GPT 5.5 und Cursor-Übernahme durch SpaceX (00:18:35) SpaceX-IPO-Prospekt: $28,5 Billion TAM (00:24:36) GPT 5.5 überholt Claude Opus 4.7 (00:26:37) OpenAI EMEA-Chef von Airbnb (00:29:09) DeepSeek V4 und China-Industriespionage (00:34:28) Google TPU 8: Training und Inferenz getrennt (00:38:47) Anthropic + Freshfields: Legal AI (00:43:05) OpenAI Super-App, USVC ab $500 (00:51:00) Layoffs: Meta, Microsoft, Snap (00:58:37) Tesla Earnings und FSD-Hardware-Lüge (01:12:34) Earnings: ServiceNow, SAP, Intel, Samsung-Streik (01:19:00) Polymarket: Heizlüfter-Betrug (01:31:18) Talon One: €750 Mio. Exit an Adyen (01:38:50) Aleph Alpha wird von Cohere übernommen Shownotes SpaceX sichert sich Kaufrecht für Cursor - ft.com XAI prüfte Kooperation mit Mistral und Cursor - businessinsider.com SpaceX-IPO: KI als größte Chance im Prospekt - reuters.com SpaceX: KI-Datenzentren im All nicht rentabel - reuters.com OpenAI GPT 5.5 und ChatGPT als Super-App - techcrunch.com OpenAI holt Airbnb-Manager als EMEA-Chef - bloomberg.com Tencent und Alibaba verhandeln DeepSeek-Investment - theinformation.com Weißes Haus wirft China industriellen KI-Diebstahl vor - ft.com Google TPU 8: Eigener Inferenz-Chip - wsj.com Google Cloud: Neue TPU-Chipreihe vorgestellt - bloomberg.com Anthropic und Freshfields: Legal-AI-Deal - ft.com USVC- xcancel.com Meta entlässt 10% für KI-Fokus - cnbc.com Microsoft: Abfindungen für 7% der Belegschaft - wsj.com Microsoft: Abfindungen für 7% der US-Belegschaft - ft.com Tesla Q1 2026 Earnings - wsj.com Tesla: Ärger mit frühen Kunden wegen FSD - marketwatch.com Musk kauft eigene Cybertrucks über Firmen - teslarati.com Air Force kauft Cybertrucks als Schießziele - fortune.com ServiceNow -14%: Iran-Krieg trifft Subscriptions - cnbc.com SAP Q1: Cloud-Revenue +27% - seekingalpha.com Intel-Aktie steigt durch KI-Boom über Dotcom-Niveau - ft.com Samsung: 30.000 streiken für KI-Gewinne - bloomberg.com Polymarket: Heizlüfter manipuliert Wetter-Wette - xcancel.com Polymarket startet gehebelte Perpetuals - cnbc.com Meta trackt Mausbewegungen für KI-Training - reuters.com Meta-Mitarbeiter empört über Überwachung - xcancel.com Angermayer: Enhanced Games als SPAC - xcancel.com FBI ermittelt gegen NYT-Reporterin - nytimes.com Talon One: €750 Mio. Exit an Adyen - manager-magazin.de Personio erstmals profitabel - handelsblatt.com Telekom erwägt volle T-Mobile-Übernahme - bloomberg.com Glöckler und das OMR-Poster - linkedin.com Google investiert bis zu $40 Mrd. in Anthropic - wsj.com Cohere übernimmt Aleph Alpha, Schwarz investiert $600 Mio. - bloomberg.com

Le nouveau modèle ChatGPT 5.5. La fusion entre Cohere et la société allemande Aleph Alpha. Le « wet compute »: un nouveau principe de futurisme éprouvé en laboratoire. Discussion IA avec David Proulx. Regardez aussi cette discussion en vidéo via https://www.qub.ca/videos ou en vous abonnant à QUB télé : https://www.tvaplus.ca/qub ou sur la chaîne YouTube QUB https://www.youtube.com/@qub_radio Pour de l'information concernant l'utilisation de vos données personnelles - https://omnystudio.com/policies/listener/fr

Our 241st episode with a summary and discussion of last week's big AI news!Recorded on 04/18/2026 Hosted by Andrey Kurenkov and Jeremie HarrisFeel free to email us your questions and feedback at andreyvkurenkov@gmail.com and/or hello@gladstone.aiRead out our text newsletter and comment on the podcast at https://lastweekin.ai/In this episode:Anthropic released Claude Opus 4.7 with improved benchmark performance, new reasoning controls, better vision and memory, and a detailed system card discussing deception risk, evaluation-awareness steering, and a training bug that accidentally supervised chain-of-thought in 7–8% of episodes.Meta unveiled its closed Muse Spark model and “contemplating mode,” highlighting test-time scaling, thought compression, large infrastructure plans like the Hyperion data center, and findings that it shows unusually high evaluation awareness.OpenAI introduced limited-access GPT 5.4 Cyber for defensive security teams and rolled major Codex updates including computer use, browser and plugins, image generation, and long-horizon task scheduling; competing agent products also launched from Anthropic, Canva, and Adobe.Business, policy, and safety news included continued government blacklisting litigation affecting Anthropic, CoreWeave compute deals, Perplexity revenue growth tied to agents, a potential Cohere–Aleph Alpha merger, attacks targeting Sam Altman and OpenAI, AI propaganda trends, and new alignment research on automated weak-to-strong supervision and steering evaluation awareness.Timestamps:(00:00:10) Intro / Banter(00:03:43) News Preview(00:04:14) Response to listener commentsTools & Apps(00:05:30) Anthropic releases Claude Opus 4.7, narrowly retaking lead for most powerful generally available LLM | VentureBeat(00:24:15) Meta debuts the Muse Spark model in a 'ground-up overhaul' of its AI | TechCrunch(00:34:23) OpenAI Launches GPT-5.4-Cyber with Expanded Access for Security Teams(00:39:44) OpenAI's big Codex update is a direct shot at Claude Code | The Verge(00:42:10) Anthropic launches Claude Design, a new product for creating quick visuals(00:42:30) Anthropic's New Product Aims to Handle the Hard Part of Building AI Agents | WIRED(00:42:54) Canva's AI 2.0 update goes all in on prompt-powered design tools | The Verge(00:43:06) Adobe's new AI Assistant marks a ‘fundamental shift' in creative work | The Verge(00:43:38) Gemini can now pull from Google Photos to generate personalized images | The Verge(00:43:52) Google rolls out a native Gemini app for Mac | TechCrunch(00:44:04) Chrome now lets you turn AI prompts into repeatable ‘Skills' | The VergeApplications & Business(00:44:22) Anthropic loses appeals court bid to temporarily block Pentagon blacklisting(00:49:07) Jeff Bezos' AI lab poaches xAI cofounder Kyle Kozic from OpenAI. | The Verge(00:51:39) Perplexity's Shift to AI Agents Boosts Revenue 50%(00:53:53) Anthropic Agrees to Rent CoreWeave AI Capacity to Power Claude(00:57:32) Canada's Cohere, Germany's Aleph Alpha reportedly in merger talks(01:04:23) ChatGPT has a new $100 per month Pro subscription | The Verge(01:05:10) OpenAI has bought AI personal finance startup Hiro | TechCrunch(01:07:03) Allbirds announced a switch from shoes to AI and its stock jumped 600 percent | The VergeProjects & Open Source(01:07:26) HY-World 2.0: A Multi-Modal World Model for Reconstructing, Generating, and Simulating 3D Worlds + Lyra 2.0: Explorable Generative 3D WorldsPolicy & Safety(01:19:12) Daniel Moreno-Gama is facing federal charges for attacking Sam Altman's home and OpenAI's HQ | The Verge(01:20:15) Duo accused of shooting at Sam Altman's house are freed; no charges filed (01:24:50) The Iranian Lego AI video creators credit their virality to ‘heart' | The Verge(01:27:19) Hundreds of Fake Pro-Trump Avatars Emerge on Social Media - The New York Times(01:27:31) The AI images Trump can't get enough of | Donald Trump | The Guardian(01:29:25) Automated Weak-to-Strong Researcher(01:43:51) Reproducing steering against evaluation awareness in a large open-weight model(01:49:53) Iran threatens ‘complete and utter annihilation' of OpenAI's $30B Stargate AI data center in Abu Dhabi — regime posts video with satellite imagery of ChatGPT-maker's premier 1GW data center(01:53:57) Wall Street Banks Try Out Anthropic's Mythos as US UrgesSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

In this episode of the Crazy Wisdom Podcast, host Stewart Alsop sits down with Mauro Schilman, CTO and Co-founder of Tuki, the distribution standard for the AI agent era in travel, for a wide-ranging conversation that moves from the joys of international travel and the beauty of mathematics to the fast-evolving world of AI and large language models. Mauro shares his background as a math Olympiad competitor and later a coach, his time training coding models at the AI company Cohere, and his thoughts on how frontier models are progressing — or plateauing — at the foundational level while innovation accelerates at the application layer. The two also get into the mechanics of agentic AI, MCP and agent-to-agent protocols, hierarchical memory systems, red-green test-driven development as a powerful coding workflow, and the philosophical murkiness of open-source AI. They wrap up discussing Tuki Travel's mission to build AI-ready infrastructure for the travel industry, connecting hotels, suppliers, and online travel agencies to prepare for the coming wave of agentic commerce. You can learn more about Tuki Travel and reach out to the team at tukiclub.com.Timestamps00:00 - Stewart welcomes Mauro Schilman, CTO and Co-founder of Tuki Travel, who shares how traveling since age 15 through high school exchanges opened his mind to cultural similarities and differences.05:00 - Mauro explains Math Olympiad coaching culture and mentorship, noting LLMs now solve competition-level problems while Terence Tao explores AI assisting frontier unsolved mathematics.10:00 - Discussion turns to ChatGPT revealing Mauro's birthdate unprompted, exposing opaque application layers, preference tuning, and system prompts hidden within closed models.15:00 - Mauro argues true open source AI requires full training data, annotation protocols, and alignment processes, not just model weights, while scaling laws appear to be slowing.20:00 - Hierarchical memory models replace flat vector databases, using three-level retrieval systems improving context accuracy as knowledge management becomes AI's core challenge.25:00 - Mauro describes travel's fragmented infrastructure of aggregators, bed banks, and intermediaries, explaining Tuki builds agent-ready unification protocols for AI commerce.30:00 - MCP versus API debate clarifies natural language capability descriptions help agents consume services, while agent-to-agent communication embeds negotiating agents inside supplier systems.35:00 - Hallucinations and consumer trust block agentic payments, industries must build mistake-resilience into bookings before autonomous agent transactions become viable.40:00 - Mauro reveals red-green test-driven development methodology where agents write failing tests first then implementations, creating Oracle verification loops dramatically improving code quality.45:00 - Blockchain's potential for transparent distributed AI training discussed, distinguishing democratization from decentralization while stable coins and regulatory momentum build toward agentic commerce infrastructure.Key Insights1. Travel broadens perspective by revealing both universal human similarities and deep cultural differences. Mauro Schilman began traveling at fifteen through math olympiad competitions and found that people across the world share fundamental traits while also being shaped in profoundly different ways by their cultures. This tension between sameness and difference is what makes travel meaningful.2. Mathematics transitions from structured problem-solving in olympiads to genuine uncertainty in graduate school and research. Olympiad problems are carefully designed with elegant solutions meant to encourage creative thinking, but once a mathematician enters academia, the answers are unknown and the work becomes navigating that uncertainty.3. AI is now assisting mathematicians at the frontier, not just solving olympiad-level problems. Terence Tao, one of the greatest living mathematicians, has written publicly about how AI tools can help tackle unsolved problems, though the role of AI remains assistive rather than independent at the research level.4. Large language models are not truly transparent even when described as open source. Releasing model weights alone does not reveal the training data, annotation protocols, alignment tuning, or system prompts that shape model behavior. Real openness would require access to the entire pipeline.5. Memory and retrieval remain core unsolved challenges in AI systems. Researchers are moving from flat vector database approaches toward hierarchical memory structures with roughly three layers, which improves retrieval accuracy and reduces how much context gets consumed with each search.6. The travel industry is structurally unprepared for AI agents. A hidden web of bed banks, aggregators, and aggregators of aggregators sits between hotels and consumers, each taking a fee. Tuki Travel is building infrastructure to unify this distribution layer and make it consumable by AI agents through protocols like MCP and emerging agent-to-agent communication standards.7. Test-driven development using a red-green approach significantly improves AI-generated code quality. By asking the model to write failing tests before writing any implementation, developers create a verification oracle that guides the model toward correct solutions and avoids the bias of writing tests that simply confirm existing flawed code.

Florian and Esther discuss the language industry news of the past few weeks and Slator's newly launched website, which reflects a clearer positioning around research, advisory, consulting, events, and market intelligence.The duo breaks down the 2026 Slator Index, highlighting that while revenues appear to have grown, this does not signal real market expansion. Instead, growth is concentrated among a few large players, often driven by acquisitions, while many companies report declining revenues.Florian touches on the RWS–Cohere strategic partnership, with RWS strengthening its technology stack by integrating advanced AI translation, while Cohere gains enterprise distribution. The move reflects a broader trend of companies recognizing they cannot build everything in-house.Off the back of Slator's Data-for-AI Market Report, Florian sees AI data services as a major growth opportunity. He explains that the industry's bottleneck has shifted from building models to making them usable in real-world settings. Esther notes growing interest from companies exploring acquisitions and investments in this space.Esther wraps things up by talking through recent M&A and funding deals, including Star7's private equity buyout, GlobalComix's expansion into manga localization with the acquisition of INKR, and VoiceLine's EUR 10m funding round in voice AI.

In this episode of Tank Talks, Matt Cohen and John Ruffolo break down the latest developments shaping Canada's tech landscape, from AI policy and government regulation to talent flight and emerging cybersecurity risks. They discuss proposals to restrict AI chatbot access for minors, the broader implications of tech regulation in a fast-moving market, and the controversial idea of imposing an exit tax on Canadians who leave for the U.S. for work.The conversation then turns to the next wave of AI competition and enterprise transformation. Matt and John unpack Cohere's reported talks to merge with a German AI company as part of a broader push around sovereign AI, data infrastructure, and enterprise model deployment in Europe. They also debate Anthropic's Claude Mythos preview and whether its reported ability to uncover zero-day vulnerabilities represents a real cybersecurity breakthrough or clever marketing. From there, they explore the rise of agentic AI inside large enterprises, where token allocation, workflow automation, and AI agents are becoming real boardroom priorities. The episode closes with Hootsuite founder Ryan Holmes returning as interim CEO, prompting a broader discussion about founder-led turnarounds, SaaS disruption, and how AI is reshaping leadership across the tech sector.Tune in for a sharp breakdown of the policy decisions, market shifts, and AI developments that could have a lasting impact on Canada's innovation economy.The Blanket AI Ban Proposal for Youth in Canada (00:57)John and Matt debate the implications of a blanket ban on AI chatbots for individuals under 16 in Canada, exploring how this could hinder youth innovation and global competitiveness, especially when countries like China are advancing rapidly in AI development.Balancing AI Privacy Concerns with Innovation (01:39)John shares his thoughts on the growing debate around AI privacy laws and whether Canada should follow the EU's model of regulation or take a more pragmatic approach. The conversation touches on the risks of banning AI technologies without considering the broader impacts on tech development.Patrick Bette's Proposal for Exit Tax on Canadians Moving to the U.S. (04:56)Matt and John discuss the controversial proposal from Patrick Bette to charge an exit tax on Canadians who leave for the U.S. to work, aiming to recover the public investment in their education. They debate whether this idea is practical and whether it reflects a misunderstanding of the challenges facing Canada's youth.Cohere's Strategic Merger with German AI Player (11:12)Matt and John talk about Cohere's potential merger with a German AI company and its implications for Canada's AI sovereignty. John examines the strategic motivations behind this move and whether this type of cross-border alliance could position Canada as a leader in AI innovation.AI's Role in the Corporate World: From Job Replacements to New Capabilities (19:00)A major topic in the episode is how AI is shifting from being seen as a tool for job replacement to one that unlocks new capabilities in various industries, including healthcare, banking, and retail. John and Matt delve into the emerging concept of internal AI agents and the complexities of managing compute resources in organizations.Hootsuite's CEO Shake-Up: Ryan Holmes Returns (21:56)The episode wraps up with a discussion on Hootsuite's recent leadership change, where founder Ryan Holmes returns to the helm as interim CEO. Matt and John explore the implications of this shift, especially in the context of the current AI-driven market disruptions.Connect with John Ruffolo on LinkedIn: https://ca.linkedin.com/in/joruffoloConnect with Matt Cohen on LinkedIn: https://ca.linkedin.com/in/matt-cohen1Visit the Ripple Ventures website: https://www.rippleventures.com/ This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit tanktalks.substack.com

Die Debatte um Anthropics Mythos-Modell geht weiter: Goldman Sachs testet es, das britische Cybersecurity-Institut bestätigt neue Fähigkeiten, Kritiker sprechen von Fear-Mongering. OpenAIs Chief Revenue Officer wendet sich in einem internen Memo an die Belegschaft und erklärt, warum Anthropic angeblich schwächer sei – aufgeblähte Umsätze, zu wenig Compute, zu enger Fokus. Am Secondary Market verliert OpenAI an Nachfrage, Anthropic steigt auf Platz 1. Zwei Anschläge auf Sam Altmans Haus in San Francisco – Altman nutzt seinen Blogpost auch für PR gegen den New-Yorker-Artikel. OpenAI lobbyiert für Haftungsfreiheit bei KI-Schäden und stärkt die Amazon-Allianz gegen Microsoft. SpaceX-Segmentzahlen enthüllt: $19 Mrd. Umsatz, Starlink macht $7,2 Mrd. Profit, xAI verbrennt $14 Mrd. Cash. CoreWeave schließt Deals mit Meta und Anthropic. Aleph Alpha verhandelt Fusion mit Cohere. Iran fordert Krypto-Maut am Hormuz. Axel Springer streicht "Vereinigtes Europa" und "sozial" aus seinen Grundsätzen. Orbán verliert in Ungarn. Unterstütze unseren Podcast und entdecke die Angebote unserer Werbepartner auf ⁠⁠⁠⁠⁠⁠doppelgaenger.io/werbung⁠⁠⁠⁠⁠⁠. Vielen Dank!  Philipp Glöckler und Philipp Klöckner sprechen heute über: (00:00:00) Mythos-Debatte (00:09:29) IPO-Rennen & OpenAI-CRO-Memo (00:25:08) Anschlag auf Sam Altmans Haus (00:40:00) SpaceX-Segmentzahlen: $19 Mrd. Umsatz (00:51:43) Hörerfrage: KI-IPOs im MSCI World (00:57:49) Aleph Alpha und Cohere (00:59:17) Zuckerberg baut AI-Avatar von sich selbst (01:06:40) Iran: Krypto-Maut an der Straße von Hormuz (01:10:45) Jude Law wirbt für Legora, Springer kippt Grundsätze (01:21:30) Orbán verliert in Ungarn Shownotes Podcast Empfehlung: Albrecht von Sonntag Radikaler Optimismus gegen Big Tech - youtu.be AISI: Bewertung von Mythos Cyber-Fähigkeiten - aisi.gov.uk Goldman nutzt Mythos für Cyberrisiken - bloomberg.com Anthropic Revenue korrigiert: $30 Mrd. um $8 Mrd. erhöht - xcancel.com Angriff auf Altmans Haus: Mordversuch angeklagt - edition.cnn.com Altmans Haus erneut angegriffen - sfstandard.com Sam Altman Post 1 - xcancel.com Sam Altman Post 2 - xcancel.com OpenAI will nicht für KI-Schäden haften - t3n.de OpenAI lobt Amazon-Allianz, kritisiert Microsoft - cnbc.com xAI-Ausgaben treiben SpaceX auf $5 Mrd. Verlust - theinformation.com SpaceX-Zahlen zeigen Starlink-Abhängigkeit - theinformation.com CoreWeave schließt Mehrjahresvertrag mit Anthropic - theinformation.com Aleph Alpha und Cohere verhandeln Fusion - handelsblatt.com Meta AI Zuckerberg - ft.com Chamath: Betrifft gesamte Tech-Branche - xcancel.com Iran fordert Krypto-Maut in der Straße von Hormuz - fortune.com Trump-Kryptoprojekt World Liberty: Investorenaufstand - bloomberg.com Sebastian Kurz: Vom Kanzler zum KI-Gründer - businessinsider.de Jura wird wieder attraktiv - linkedin.com Wie Axel Springer die USA umarmt - theguardian.com Springer streicht Pro-Europa aus Statuten - linkedin.com Vance hilft Orbán, wird verspottet - spiegel.de

Prognosemärkte sorgen wegen einiger verdächtiger Aktivitäten rund um den Iran-Krieg für Diskussionen. Und: Deutschland und Kanada wollen die Entwicklung einer souveränen KI gemeinsam vorantreiben.

Apr 05

Ben Faes, CEO of RWS, joins SlatorPod to talk about the markets' perceptions of LSIs, the company's AI strategy, and how RWS is repositioning itself for long-term growth.Ben positions RWS as a technology-led partner helping enterprises operate globally, from enabling multilingual communication to protecting intellectual property and improving market understanding.The CEO highlights the rapid acceleration of innovation and the democratization of AI, where individuals and companies can now build and deploy solutions at unprecedented speed. He argues that the real opportunity lies in using these capabilities more effectively, rather than applying them to low-value tasks.He describes the partnership with Cohere as a fundamental shift, with RWS integrating Cohere's models into its Language Weaver Pro platform, moving beyond traditional, segment-based translation toward context-aware, LLM-driven solutions.Beyond translation, Ben sees strong growth in AI data services, especially in areas like cultural intelligence and multimodal training, where human expertise remains critical. Internally, RWS has reorganized into three divisions — Generate, Transform, and Protect — to better align with customer needs, buyer personas, and evolving use cases.Despite short-term uncertainty, Ben remains optimistic, noting that new AI-driven services and products account for a growing share of revenue and signal how quickly the market is evolving.

Hey y'all, Alex here, let me catch you up!Jensen Huang went on Lex and said AGI has been achieved. We'll get to that.The biggest demo moment: Gemini 3.1 Flash Live launched - Google's omni model that sees, hears, and searches the web in real time. We tested it live and I said “what the f**k” on air. It was really impressive!Google Research also dropped TurboQuant (6x KV cache compression) which crashed Samsung and Micron stocks - we had Daniel Han from UnSloth help us make sense of why that's overblown. OpenAI killed Sora - the app, the API, and the $1B Disney deal. Claude felt noticeably dumber this week AND max account quotas are melting as 500+ people confirmed on my X and Reddit. We have an official word from Anthropic as to why. Mistral launched Voxtral TTS (open weight, claims to beat ElevenLabs), Cohere shipped an ASR model, and Google's Lyria 3 Pro now generates full 3-minute music tracks inside Producer AI.This and a lot more in today's episode, let's dive in (as always, show notes and links in the end!) ThursdAI - Let me catch you up! Gemini 3.1 Flash Live: The Real-Time AI Companion Is HereGoogle dropped a breaking news on the show today, with Gemini 3.1 Flash - LIVE version. This one is an omni-model, that means it can receive text/audio/video on input and respond in text and voice. It has Google search grounding, and it felt... immediate! I was blown away, really, check out the video, the speed with which it was able to “see” me, respond to my query, look up something on the web, was mind blowing. I don't often get “mind blown” anymore, there's just too many news, but this one did the trick! With the pricing being around 10x cheaper than GPT-real-time, and the Google search grounding being super fast, I can absolutely see this model being hooked up to... robots (like ReachyMini), SmartGlasses that can see what you see, and a bunch more! Gemini Live is available on Google AI studio and has been rolled out globally inside the Google Search app! So now when you pull up the Google Search app, just open it and point at anything. Truly a remarkable advancement.Google research publishes TurboQuant - 6x reduction in KV cache with 0 accuracy lossGoogle research posted some work (based on an Arxiv paper from almost a year ago) that shows that with geometry tricks, combining two other techniques like PolarQuant and QJL, they are able to compress the KV cache of running LLMs by nearly 6x, and show an 8X speed up for model inference with zero accuracy loss. If you ever watched silicon valley the HBO show, this sounds like the fictional middle-out algorithm from PiedPiper. If this scales (and that's a big if, we don't know if this applies to other, bigger models yet), this means significant decreases in memory requirements to run the current crop of LLMs for longer context. The claim is big, so we'll continue to monitor if this indeed scales, but the most interesting thing about this piece of news is, that it broke the AI bubble and went to wall street, with finance brows deciding that this means that memory will not be needed as much any more and it tanked Samsung and Micron stocks. Which I found particularly ridiculous on the show, did they not hear about Jevons Paradox? This is reminiscent of the DeepSeek R1 saga that tanked Nvidia stocks over a year ago. Daniel Han from Unsloth, who joined us on the show, pointed out that the approach is mathematically interesting even if it's not necessarily better than existing open-source techniques like DeepSeek MLA. LDJ noted that the baseline comparison (16-bit KV cache) isn't really fair since most production systems are already compressing beyond that. Yam implemented it himself and confirmed the speedups are real, but so is the trade-off.Anthropic updates: Opus dumber? Quotas lower! Injunction won! Computer.. used. Anthropic folks, especially on the Claude code side are shipping like crazy, we won't be able to cover all the updates, but there was a few notable things I have to keep you up to date on. Claude Opus seems to be getting “dumber”, againI have to talk about this because it affected my work directly this week and hundreds of people confirmed the same experience.I use Claude Opus for my standard ThursdAI prep workflow — generating the TL;DR with 10 bullet points and an executive summary for every topic we cover, creating episode pages, etc. The format has not changed for over a year and yet this week I asked for 10 factoids. I got 4. It says “10” right there in the prompt. Four bullet points. On the website builder, I've asked Opus to create a page for last weeks episode, and instead of adding it to the other episode, Opus decided to ... replace the last episode with this one. This would be funny if it wasn't sad. This is Opus 4.6 we're talking about, not some quantized open source LLM from last year! The reason is unclear, and it's not only me, Wolfram noticed that it's easier to see these types of things in other languages and that for the last week Opus would forget to add Umlauts in German!? and Yam also felt it. Pro/Max plan quotas burning up, Anthropic confirmed that they are tightening them for “peak hour” usageThis week, so many people started posting that something is wrong with their Claude Codes, I did a survey, and it blew up. Hundreds of people replied and confirmed that for the first week, they are hitting their session quotas on Pro and 20x $200/mo MAX accounts much much quicker than before. When I say much quicker, I mean, some fokls have hit the quota in as little as 5 minutes. While some others had no issues. I personally btw did not have this. A few days later, Thariq from the Claude code team, and later an official post, confirmed that Anthropic had been rolling out a “tightening” of the Pro/Max accounts to accomodate for growth. This is of course, a huge bummer to the folks who pay $200/mo for the 20x max tier, as they tend to run agents and subagents overnight. But here's the thing, I don't think that folks from Anthropic see what we see, some folks got no issues with hitting quota, and some are barely able to use their subscription. I hope that they will find and resolve these bugs quick, because some folks are switching to Codex, and the Anthropic IPO is coming up! I will say, I don't envy Thariq's job, he's doing it gracefully, and maybe one of the only ones in Anthropic that does it at all. Judge granted Anthropic an injunction against DoW and the whole “Supply chain risk” designation!Just in as I'm writing this, a district judge in CA, granted Anthropic an injunction against being designated as a supply-chain-risk company. If you haven't been following, the US Department of War, specifically Pete Hegseth, threatened and then designated Anthropic as a supply chain risk company, while us president Trump “fired” Anthropic and banned its use in any gov agencies. Well, no so fast says Judge Lin, from CA District court. In this Order, she shows that Dept. of war didn't meet any legal requirements for this designation. It's really a fascinating read, but the highligth is this: When asked why Hegseth made a public statementthat had no legal effect and that did not reflect the immediate intent of DoW, counsel stated, “I don't know.”This is just the first court and will likely be escalated further up the judicial system. This is still developing and apparently the Pentagon declared Anthropic a supply chain risk under two different statutes, and this only affects one of them. So while it's good news, it's not over yet. Voice & Audio Explosion: Three Releases in One HourI had to hit the breaking news button mid-TLDR because three major voice releases dropped simultaneously during the show.Mistral Voxtral TTS — Mistral's first text-to-speech model, 3 billion parameters, open weight. They claim it beats ElevenLabs Flash v2.5 in human preference tests (58% win rate on flagship voices, 68% on zero-shot voice cloning). We tested it live on the show — it's decent, with emotion controls for neutral, happy, and frustrated voices. I was not super impressed tbh, it sits somewhere between the very good big labs TTS and the very small open source 82M param TTS. Cohere Transcribe — Cohere enters the ASR game with a 2 billion parameter open-source model (Apache 2.0!) that immediately grabbed the #1 spot on HuggingFace's Open ASR Leaderboard with a 5.42% word error rate, beating Whisper Large v3's 7.44%. In human evaluations, it wins 61% of the time on average, and 64% specifically against Whisper. For anyone in regulated industries needing local inference for compliance, this could genuinely replace Whisper as the default.Google Lyria 3 Pro — Google's most advanced music model is here.It can now generate full 3-minute tracks with structural control — intros, verses, choruses, bridges. We generated a ThursdAI opening theme live on the show using Producer AI, and it was... honestly not bad? It followed our instructions perfectly: drum and bass, 174 BPM, high energy podcast opener with vocals and introduction. The instruction-following was spot on. Nisten said it's the best music generation model right now. It's available to Gemini subscribers and via Producer AI and gemini, and it can even compose music from images. SynthID watermarked, royalty-free. We might actually use one of the generated tracks as a new show opener.The craziest thing is, since Google acquired Composer, the team has been shipping. I only generated the audio during the live show, but now went back there to download it for you guys, and whoah, it can now generate whole clips by using other Google tech, this is really cool! OpenAI kills SORA (and Atlas?)Last week we reported on about OpenAI's focus shift towards Codex and productivity, and this week we see the first casualty. OpenAI is killing SORA, the app, the Sora 2 and Sora 2 pro models and APIs. Many AI haters are celebrating this as through “ai videos” is dead, but honestly, this is obviously about the GPU power and the other things OpenAI needs to do to win the fight against Anthropic. OpenAI is also apparently going to IPO this year (like Anthropic) and they absolutely need to win the productivity/agents in enterprise market. As part of this shut down, the Disney + OpenAI partnership, is also dissolving, and Disney will no longer invest 1B into OpenAI.So, say bye bye to having digital selfies with Sam Altman. I've generated this SORA vid to hear from Sam himself: Atlas browser, OpenAI's native browser endeavor is supposedly also going to transform, together with Codex and OpenAI native app into one super app that includes all three according to the same memo. AGI is here according to Jensen, AGI is far away, according to ARC-AGI-3 The back to back this week can give anyone whiplash. First, Lex Friedman had Jensen Huang on the podcast, and asked him a very specific “WhenAGI” question, to which Jensen said “I believe it's already here” Then just a few short days layer, ArcPrize, released the 3rd version of Arc-AGI, Arc-AGI 3 a series of puzzle games, where humans get 100% pass-rate and the current LLM, top tier frontier LLMs, are getting less than 1%! It's an interactive, agentic reasoning benchmark designed to test human-like generalization and intelligence in novel, abstract, turn-based environments.The puzzles all look simple enough to do, and are actually fun, and while the wild claims of “AGI is not here yet” from the ArcPrize folks are quite interesting. The stated goal of the foudation is to release evaluations that are completely un-saturated, and this seems like one such thing at first glance. There's a bit of a debate in the community about the way Arc Prize went about this specific benchmark (no harnesses, raw LLM outputs), saying that humans got a “game” while the LLMs get just raw JSON and minimal and no extra tools. For context, a agentic harness startup claims to have solved 35% already of the games in ArcAGI, but that result is unverified and self reported, becuase they are an agentic harness, which ArcAGI apparently disqualifies. AI Art and DiffusionI wanted to finish but I think these are important releases so I'll include them briefly. Luma Labs Uni-1 — thinks and generates pixels simultaneously, #1 human preference Elo (X, Announcement)This was a surprising release, we previously seen Luma Labs do video, but this time they are posting their Uni-1 which is a… image model but it's based on an LLM, so you talk to it, iterate together until you get results. Yes, Nano Banana via AI studio is kind of like this as well ,but Uni feels a bit different. It can also generate infographics, which I haven't tried yet. You can try Uni herePhota Labs launches Phota Studio + API — a photography-focused image model with identity-preserving personalization (X, try it)There's tons of photo startups, but this one looks kind of crazy! You upload a bunch of your pictures, they train a “model” for you, and then you can create a whole bunch of images, and they do actually resemble you. Yes, Nano Banana can take a few reference pictures, but this somehow seems more accurate! You can create professional photos, fix photos you like, add others to your photos. I do feel there's a jump in capabilities here, specifically because of the personalization! Give them a try if you're not worried about them training on your pics and let me know.Modular made Flux.2 run in

Erin Gertner, vice president of the Partner Organization and SMB Sales at Cisco Canada When Cisco CEO Chuck Robbins told investors the campus and data centre refresh is at “the top of the first inning” of a multi-year, multibillion-dollar opportunity, it raised an obvious question for Canadian partners: what does that inning look like here? Erin Gertner, vice president of the Partner Organization and SMB Sales at Cisco Canada, says Canada is tracking with the global trend – and that the opportunity is being driven by a “perfect storm” of three converging forces: the largest last-day-of-support (LDOS) wave Cisco has seen in years, growing urgency around AI readiness, and increasing pressure around data sovereignty. The AI readiness gap is particularly striking. Only 7% of Canadian organizations say they’re fully prepared to deploy AI – down from 9% the previous year – while 96% say the urgency has increased. That tension is creating real opportunities for partners who can lead with outcomes rather than product. Gertner says the partners winning the biggest deals are those taking a consultative approach – running assessments, broadening the conversation beyond a like-for-like swap, and helping customers understand their full security and AI readiness posture. In one example, a security assessment nearly quadrupled the deal size compared to a straight hardware refresh. The conversation also touches on where vertical demand is hottest (financial services and healthcare are leading), how the Secure AI Factory with NVIDIA translates for mid-market partners, the role of data sovereignty in driving on-prem modernization, and what smaller MSPs should be doing to get in the game. Gertner’s advice to partners who haven’t started? Reach out to your Cisco partner account manager or distributor and get access to the PXP data – the opportunity is there, and Cisco wants to make it easy to find. Read Full Transcript Robert Dutt: Hello and welcome to In The Channel from ChannelBuzz.ca, bringing news and information to the Canadian IT channel community for the last 16 years. I’m Robert Dutt, editor of ChannelBuzz.ca, and as always, your host for the show. On Cisco’s most recent earnings call, CEO Chuck Robbins called the campus and data centre refresh the top of the first inning of a multi-year, multi-billion dollar opportunity. Double-digit growth in networking, six consecutive quarters. But that’s the global picture. What does the first inning look like in Canada? My guest today is Erin Gertner, VP of the Partner Organization and SMB Sales for Cisco Canada. Erin sees what she calls a “perfect storm” converging right now – a massive wave of aging infrastructure hitting last day of support, growing urgency around AI readiness, and increasing pressure around data sovereignty. We get into what Cisco Canada is seeing on the ground, where partners are finding the most traction, and what separates the ones winning those deals from the ones leaving the door open for somebody else. Let’s get right into it. My chat with Erin Gertner. Erin, thanks for taking the time. I appreciate it. Erin Gertner: Thank you so much, and thank you for having me, Robert. It’s nice to see you. Robert Dutt: Nice to see you as well. It’s been a little while since Partner Summit when last we sat down, but I wanted to chat because of Chuck’s comments on the earnings call, talking about the top of the first inning on a multi-year, multi-billion dollar opportunity around campus refresh. Double-digit growth in networking for six consecutive quarters. That’s the global picture. I guess to throw it open, what does that top of the first inning look like from where Cisco Canada sits? Are we tracking with the US on this one? Are we still back in spring training? What does the Canadian opportunity look like in this moment? Erin Gertner: I think we’re seeing something very similar to what Chuck spoke about on the earnings call. We are seeing a multi-year, multi-billion dollar refresh cycle taking place here in Canada. And I think it is the perfect storm of three things coming together. One, we have a lot of aged infrastructure out there. Sometimes we call it last day of support, or LDOS. When we look in our portfolio, we’ve got the largest LDOS opportunity that we’ve had in many, many years this year and next year. We’ve been working with many of our partners as well as our account teams to start going out and pursuing those opportunities because we really do need to get in front of them. But we’re also seeing the dynamics of a few other things taking place. One is AI readiness. I think you probably heard in our earnings call, Chuck talk about the success that we’re having in AI. A lot of that today is really centred in the world of the hyperscalers. In our last earnings call, I talked about doing over $2 billion worth of infrastructure with the hyperscalers. So there’s this huge influx of demand around AI. But where we haven’t really scratched the surface is AI in the enterprise. The hyperscalers are very well prepared, but now we’re starting to see this big wave of enterprise deployment, or at least enterprises thinking about the use cases and the ROI, because it is a board-level conversation. And then lastly, and this is probably a topic you hear a lot about working in Canada, is around digital resilience and data sovereignty. You need a modernized, secure network in order to deploy AI, and the network is more critical than it’s ever been as you think about the role it’s going to play in the next few years. The ability to fuse together security into the network is really unique and core for Cisco and driving refresh. I often talk to partners about the LDOS opportunity, and we used to get the question a lot of, “Why would a customer upgrade?” or, “How do I have this conversation with a customer?” because their response is often, “It all still works. Why bother?” I think AI especially is really giving them that reason to modernize, because while their network may work, it wasn’t necessarily built to run the applications that they’re going to need today and in the future. So it’s a really compelling conversation, and we’re seeing huge uptake and demand in networking. Robert Dutt: You touch on the customer size, especially on the AI side of things. Looking across the Canadian market in terms of customer size, vertical, geography – is the refresh opportunity relatively evenly distributed, or is it concentrated? Where’s the heat at right now? Erin Gertner: It’s been interesting. All of our account teams, some of which are verticalized, others which are organized geographically, talk a lot about where they’re seeing refresh opportunity. A great example is what we’re hearing from financial services organizations. We had that long period of COVID, and then there’s been a ton of conversation around return to office. Our financial services team will tell you that there’s massive demand because if you listen to what the banks or insurance companies are doing, they’re asking people to come back to the office. Those networks, many of which were built in 2018 or 2019, can’t support the applications that are being driven in today’s world. They can’t even support the number of people they have anymore. [A lot of those organizations saw a boom.] So there’s a huge network refresh taking place right now in that specific vertical. We’re also hearing a lot about mission-critical verticals like healthcare, where uptime is hugely important and security and resilience are top of mind. But it’s really spread throughout. Many companies had a long period of time where they spent a good majority of their budget on work from home and getting people set up for different use cases. Now that we’re living in this hybrid world, or a lot of organizations are back to work, that’s putting a huge change in demand on what is being asked from the network, plus everything that’s happened from the AI perspective. Robert Dutt: You bring in a lot of different threads in terms of things that are driving this – AI readiness at the top of the list, aging infrastructure, data sovereignty, security modernization, probably a few more. What’s actually leading the charge in this moment for the conversations you’re having with Canadian partners and customers? I’m curious if one of those things is the leader and the others follow, or if there’s really a convergence where this is a big pile of conversation topics at the same time. Erin Gertner: I think it’s a big pile of conversation topics at the same time, and it also depends on the partner you talk to and how they’re approaching a customer. Every partner has got a really interesting and different approach, especially when it comes to AI, and I love that about our partner community. A lot of them are taking, for example, an advisory services-led approach, or they’re taking the approach of – I hate this expression, but it’s one that makes sense – eating your own dog food. I was with a partner last week and they were talking about a lot of the work that they had done to embed AI into their own workflows. Then they were taking their success out into the market and starting new conversations with customers they hadn’t historically had access to. All of that was leading to a network refresh conversation, because customers are excited about the opportunity with AI, and then the partner was able to embed the question around, “Well, are you ready? Do you have the right infrastructure in place?” The conversation often is bigger than that, and obviously security is a huge area of concern when it comes to AI. I think that’s where Cisco is very uniquely positioned to win in this space. We’re seeing a lot of our competitors try to bring network and security together, and we’re really the only organization who can truly embed network and security together and then traverse it from the campus to the data centre. Robert Dutt: To your point on dog food, I learned from a partner years ago that the way to phrase it is “drinking one’s own champagne.” Erin Gertner: Oh, I like that expression a lot better. Thank you for that. Robert Dutt: Let’s talk about the AI side of things. Cisco’s own AI Readiness Index showed that 7% of Canadian organizations feel they’re fully prepared to deploy AI, and that’s actually down a couple of points from 9% in 2024. 96% say it’s more urgent than ever. That’s a pretty big gap. How’s that tension showing up in the conversations that partners are having with their customers? Erin Gertner: I’ve spoken to a lot of partners in the last little while, and again, each are taking a very individual approach. I think leading with outcomes and that consultative mindset – and it looks very different for each partner – but they’re all trying to understand what outcome a customer is trying to deliver, or what is the ROI, or what is that metric that’s going to help move a CEO’s agenda forward, or help them understand how they can build a true business case to build out a full AI deployment. It’s hard, right? We’re going through our own transformation at Cisco. We’ve got a team of individuals who work with us internally building out our AI workflows, and even on my own team, we’re trying to do all these things to help our team adopt AI tools to make their lives easier and more efficient. You often hear that somebody’s job is not going to be taken by AI – it’ll be taken by somebody who knows how to use AI. It is even more critical than ever that organizations figure it out. A lot of our partners have deployed some interesting things for themselves or worked through really interesting consulting engagements where they have use cases they can take out to market and help customers build that business case for themselves. They need to start small, they need to define what success looks like, and I think many customers have a long road there, but there’s certainly hope that we’re headed in the right direction. Robert Dutt: Raj, the president of Cisco Canada, wrote an op-ed recently saying that Canadian businesses risk – I think the quote was – “Blockbuster-style failure” without having the right AI infrastructure. For a partner who’s sitting across the table from a customer who feels that urgency but hasn’t really started yet, what do you counsel that partner to advise the customer on? What’s the practical starting point? Where do you begin? Erin Gertner: It’s tough. Again, it depends what type of customer they are and what their use case looks like. But I think for that customer, it’s really leaning back to outcomes – what is going to demonstrate success for that organization? The last thing you want anybody to do is go out and deploy an AI application and see absolutely no success out of it. That will move that executive’s agenda back probably a couple of years. But we are also really encouraging partners to talk through: Are they ready? You can have the best use case out there, but do you have a good data strategy? Do you have a good security strategy? Have you thought about modernizing your network? Is sovereignty important to you? And if it is, do you want to start thinking about potentially building that on-prem, or taking a different approach than maybe what you have historically done, because there are new considerations being layered on top of all of that. Robert Dutt: Talk to me about the Secure AI Factory side of things. Tim Coogan called it the partner opportunity of this year. I’m curious how that translates practically for Canadian partners. Is this a play mostly for the big SIs, or are you finding mid-market partners who are finding a role in the AI infrastructure buildout? Erin Gertner: I think it’s a little bit of both. We’re having conversations around Secure AI Factory with some of our largest partners because it is really unique. Our relationship with NVIDIA is truly one of a kind, and we’re actually creating products together. I know everybody has done a great job of partnering with NVIDIA in the market, but our relationship with them is a little bit different. What I love about the whole notion of Secure AI Factory is the fact that it’s everything built together. We make it really easy. We’ve pre-built all the CVDs. We’ve essentially created a blueprint for partners and customers to go out and deploy an entire AI pod. That includes everything from networking to servers to security to observability. We can even include storage, even though we don’t make it – we’ve got a bunch of great storage partners. Is it going to work for a small customer being serviced by a small partner? Probably not. It might be outside the scope of what they’re doing. But for mid-sized customers who are running interesting workloads and they want them on-prem, and especially for bigger customers who want to scale and deploy really quickly, or partners who have a ton of depth and capability in that space, the Secure AI Factory is a great solution. Robert Dutt: For a Canadian partner who’s looking at this refresh opportunity, where are you seeing the most traction in terms of the technology stack? Is it campus switching, data centre modernization, Wi-Fi, security? What’s the entry point that’s helping partners produce pipeline right now? Erin Gertner: We’ve done a lot of work with partners. We’ve got a tool called PXP – I think you’ve probably had some exposure to it – but we’ve been doing quite a few workshops with our partners to help them understand where their opportunity is. PXP does a great job of being very data-rich and data-centric. As we go through the enablement with partners, it gives them a good sense of what their refresh opportunity looks like. Then we are trying to make sure we enable them around the broader conversation. You don’t want to just be refreshing a switch for a switch. Our best partners are taking that data and – again, to your question, some partners, let’s say their history was really in the data centre – data centre networking is probably their biggest opportunity because that’s where they’ve sold the most in the past. For more broad-scale partners, it could be a combination of two or three different things. What we’re really trying to coach them to do is take that opportunity and don’t refresh a switch for a switch. Help the customer understand what outcome they’re trying to achieve. Do they have the right security posture? What’s their Wi-Fi strategy? What’s their device strategy? We’re trying to help them take that data and broaden the conversation into something that’s more outcome-driven. Our best partners are doing an excellent job of that and building really big, interesting deals alongside their customers. Robert Dutt: In doing that, when you’re looking at the services layer, are there any particular areas that you find are especially productive? Assessments, design, migration, managed services post-deployment – where are partners getting the most return from focusing their energy? Erin Gertner: Consulting services has been a huge one. We’ve got a great assessment program and we have some partners who are doing a great job leveraging it and seeing a ton of success. I was in a partner QBR the other day and they were giving an example of having done a security assessment with a customer that significantly broadened the scope of the deal and helped the customer understand where they had some vulnerabilities in their current infrastructure. That deal almost quadrupled in size. Partners are doing a great job with that. What we’re really trying to encourage partners to do is make sure we’ve got an adoption plan for every software deal out there upfront, because we want to make sure anything our customers buy from our partners, they have a great experience with. If they aren’t doing a good job of adopting that and showing value all the way throughout the chain, we’re not going to see a renewal at the end. The other thing we’ve been talking a lot about with our leadership team is some of what’s happening in the industry right now with some of the shortages that are industry-wide. In COVID, we saw something similar happen – a lot of supply chain constraints. Then there was this really long ingest period that happened afterwards because customers just had so much technology. We are really encouraging our partners and our teams to make sure we’re leading with services, so there is an outcome attached to the end and there is a plan with the customer to consume the technology so they can get the most out of what they’ve bought from us. Robert Dutt: We talked a little bit about the big guys, the SIs, and the opportunity around AI Factory. For the smaller partner, that long-tail 15-to-20-person MSP that’s living in Meraki and maybe doing some security, is this a real opportunity for them, or is this fundamentally a larger VAR and SI play? Where it is accessible to that SMB-focused partner, what does the on-ramp look like? Erin Gertner: It’s absolutely accessible for that SMB partner. I also have the SMB part of our business, so this conversation is very close to my heart. Given the IT skills shortage that is very dominant in the Canadian market, we are seeing a lot of customers who don’t want to manage their own network. As customers grow – let’s say they were a very small customer four or five years ago and they chose more of a consumer-grade solution at that time – as they want to move into a more enterprise-type solution with security and all the other bells and whistles embedded in it, a lot of those customers are choosing not to manage that themselves. But they want to be in the same place as their competitors, because the expectation is they grow and scale just as fast, probably faster in fact, as a big company. A lot of those companies are born in the cloud, leveraging tons of cloud applications, so the way they create their foundation is even more critical than ever. We have a bunch of great small to mid-size partners who are doing awesome things in that space and growing pretty significantly, actually gaining a lot of market share because of their agility and their ability to manage something at a cost-effective price. Robert Dutt: You mentioned the importance of data sovereignty in the conversation. The federal government has launched a call for proposals for sovereign AI data centres of over 100 megawatts, and we’ve seen Cohere get a lot of federal backing for their data centre build. Is data sovereignty a driver in this enterprise refresh, or is it a parallel conversation that’s happening at the same time? Erin Gertner: I think it’s a bit of a parallel conversation, but it’s certainly driving a huge – not even refresh – just huge modernization effort. A lot of it is centred around Canadian organizations who are worried about data sovereignty, or who are worried that sovereignty requirements might hit them in the next few years. They’re trying to prepare themselves by building out new types of data centres on-premise – new data centres to support applications coming back on-prem. While maybe they haven’t built everything on-prem today, we are seeing a massive surge in companies starting to think about what that might look like. For customers who had almost all of their applications in the cloud previously, their data centre network didn’t necessarily support the low-latency, really high-bandwidth requirements that would come into play if they start putting mission-critical applications back on-prem. We are seeing a lot of customers starting to think about what they would need to build to support sovereignty requirements, or if they’re going to continue to live in a hybrid world – which, let’s be honest, the majority of Canadian organizations are probably going to live in that world, and that’s all good – the network they have today probably doesn’t support that in the way they’d like either. Robert Dutt: Let’s talk about what you’re doing to support partners through this process. What are the incentives, enablement resources, the programs that are particularly relevant to Canadian partners who are looking at this opportunity and going after it? Erin Gertner: I think we’ve been pretty declarative about wanting to be the critical infrastructure for the AI era. We’re doing a lot of enablement with our partners. We’ve aligned our incentives, both front-end and back-end, to this opportunity. We’re doing a lot of workshops to help our partners understand where those opportunities lie and help them understand how to go out and capture them. We’ve also been running a lot of demand generation alongside our partners around our AI strategy, what that looks like, as well as showcasing the innovation that Jeetu has put forward in our portfolio around network and security coming together, because I do think it’s a great story and one that maybe not everybody knew. Some people probably think we’ve still got two different platforms with Catalyst and Meraki, where the truth is those have come together in the last year. With our acquisition of Splunk, there’s a lot that’s been infused into the network. Jeetu has also done a fantastic job of creating a really innovative security portfolio, a lot of which is actually embedded into the network layer. So there’s been a lot of education that we’ve had to do with both our partners and our customers to make sure they’re able to go out and tell that story to the market. I think Tim Coogan said this best – our job is to create that innovation, and then our job is also to help enable our partners to go out and be an extension of our sales force and help them deliver value to customers based on that innovation. Robert Dutt: What do you see as separating the partners who are winning these refresh deals from those who aren’t? What are the best partners doing differently? Erin Gertner: Again, I think really leading with that outcomes-based conversation and not just doing a like-for-like refresh. The ones who are going out and really taking a consultative approach, they’re winning a lot more and they’re winning much larger deals. I was on with a partner yesterday who was showcasing some of the work they’d been doing around AI and sharing with us some of the success they had just recently had, and they’re winning amazing deals by taking a very consulting-led approach. What we have seen in the past from certain partners is they go in and focus very much on that refresh opportunity, and then they almost leave the door open for another partner to come in and have a conversation around networking, observability, and all the other aspects around that critical infrastructure. So the best partners are the ones who are leading with the whole portfolio. I know we’re going to talk about 360 as well, but we’re really trying to incentivize our partners to build a lot of skills and technical depth around our solutions, and the ones who are really good at being able to tell the story of how our whole portfolio comes together – that “One Cisco” story that we often talk about – they’re the ones who are winning the most. Robert Dutt: If I’m a Canadian partner listening to this and I haven’t really started leaning into that refresh opportunity yet, what should I be doing about this on Monday morning when I show up to work? And looking further out, we’re in the top of the first – what do you see the second and third innings looking like here in Canada? Erin Gertner: Firstly, reach out to us. However you engage with Cisco, whether it’s through one of our distributors – who are amazing and have access to all of our tools – or reach out to your partner account manager at Cisco. We can provide all the training required on how to have the right conversation, as well as access to all the data you need to help you figure out where you should start and which customers are due for a refresh or have a refresh opportunity in the next six months. We can make it really easy for our partners to know where to spend their time and get a pretty fruitful payoff, both on the front-end and the back-end with us. What do I think the second and third innings might look like? I think we’re still really at the infancy of that. We’ve seen a few customers go down the refresh path – probably our largest customers have gone down the refresh path. Some of them have modernized networks or they’ve gotten to where they think they need to be to support AI applications. But I do think we’re going to see some of our smaller customers start to catch up. I also think we’re still really at the infancy of the success of AI. We talk a lot about the role of agentic AI and how that’s going to proliferate through organizations in the future. I don’t know that many customers have figured that out yet today. There are some who are really at the edge of innovation and who’ve done an amazing job with that, but it isn’t mainstreamed yet. As agentic AI really starts to roll out, the demands on your network and the demands around security especially become even more complex and even more critical. I think that’s going to be the next wave. A lot of companies have done a good job of finding one or two use cases, maybe small ones, that have delivered value for them in AI. But there are very few organizations – and we talked about it through the AI Readiness Index – very few organizations who have really found tremendous value from AI today, but they will in the future. Robert Dutt: I think you’ve done a great job of setting up the game for Canadian partners here. Good luck with the rest of the ballgame, and thanks so much for taking the time. Erin Gertner: Thank you. Robert Dutt: There you have it, Erin Gertner from Cisco Canada. I’d like to thank Erin for her time on this one, and thank you for listening. A couple of things that stood out to me. First, how strongly the consulting and assessment-led approach is paying off. Partners who are going in and helping customers understand the full picture – security, AI readiness, network modernization – aren’t just winning deals. They’re winning deals that are three and four times the size of a like-for-like refresh. And the other is something Erin said that I think is worth sitting with: there’s no AI without a network. Simple statement, but it reframes the entire refresh conversation for partners who aren’t sure where AI fits into what they do. If you’re enjoying In The Channel, you can find us on Apple Podcasts, Spotify, YouTube, and most podcast directories. Follow, subscribe, leave a rating or a review if you’re feeling generous. It all helps. Till next time, I’m Robert Dutt for ChannelBuzz.ca, and I’ll see you in the channel.

Explore Oracle AI Vector Search and learn how to find data by meaning, not just keywords, using powerful vector embeddings within Oracle Database 23ai. In this episode, hosts Lois Houston and Nikita Abraham, along with Senior Principal APEX & Apps Dev Instructor Brent Dayley, break down how similarity search works, the new VECTOR data type, and practical steps for implementing secure, AI-powered search across both structured and unstructured data.   Oracle AI Vector Search Fundamentals: https://mylearn.oracle.com/ou/course/oracle-ai-vector-search-fundamentals/140188/ Oracle University Learning Community: https://education.oracle.com/ou-community LinkedIn: https://www.linkedin.com/showcase/oracle-university/ X: https://x.com/Oracle_Edu   Special thanks to Arijit Ghosh, Anna Hulkower, Kris-Ann Nansen, and the OU Studio Team for helping us create this episode.   ---------------------------------------------------- Episode Transcript:   00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we'll bring you foundational training on the most popular Oracle technologies. Let's get started! 00:26 Lois: Hello and welcome to the Oracle University Podcast! I'm Lois Houston, Director of Communications and Adoption Programs with Customer Success Services, and with me is Nikita Abraham, Team Lead: Editorial Services with Oracle University. Nikita: Hi everyone! Today, we're beginning a brand-new season, this time on Oracle AI Vector Search. Whether you're new to vector searches or you've already been experimenting with AI and data, this episode will help you understand why Oracle's approach is such a game-changer. Lois: To make sure we're all starting from the same place, here's a quick overview. Oracle AI Vector Search lets you go beyond traditional database searches. Not only can you find data based on specific attribute values or keywords, but you can also search by meaning, using the semantics of your data, which opens up a whole new world of possibilities. 01:20 Nikita: That's right, Lois. And guiding us through this episode is Senior Principal APEX & Apps Dev Instructor Brent Dayley. Hi Brent! What's unique about Oracle's approach to vector search? What are the big benefits? Brent: Now one of the biggest benefits of Oracle AI Vector Search is that semantic search on unstructured data can be combined with relational search on business data, all in one single system. This is very powerful, and also a lot more effective because you don't need to add a specialized vector database. And this eliminates the pain of data fragmentation between multiple systems.  It also supports Retrieval Augmented Generation, also known as RAG. Now this is a breakthrough generative AI technique that combines large language models and private business data. And this allows you to deliver responses to natural language questions. RAG provides higher accuracy and avoids having to expose private data by including it in the large language model training data.  02:41 Lois: OK, and can you explain what the new VECTOR data type is? Brent: So, this data type was introduced in Oracle Database 23ai. And it allows you to store vector embeddings alongside other business data.  Now, the vector data type allows a foundation to store vector embeddings. This allows you to store your business data in the database alongside your unstructured data, and allows you to use those in your queries. So it allows you to apply semantic queries on business data.  03:24 Lois: For many of our listeners, "vector embeddings" might be a new term. Can you explain what vector embeddings are? Brent: Vector embeddings are mathematical representations of data points. They assign mathematical representations based on meaning and context of your unstructured data.  You have to generate vector embeddings from your unstructured data either outside or within the Oracle Database. In order to get vector embeddings, you can either use ONNX embedding machine learning models or access third-party REST APIs.  Embeddings can be used to represent almost any type of data, including text, audio, or visual such as pictures. And they are used in proximity searches.  04:19 Nikita: Now, searching with these embeddings isn't about looking for exact matches like traditional search, right? This is more about meaning and similarity, even when the words or images differ? Brent, how does similarity search work in this context? Brent: So vector data is usually unevenly distributed and clustered. Vector data tends to be unevenly distributed and clustered into groups that are semantically related. Doing a similarity search based on a given query vector is equivalent to retrieving the k nearest vectors to your query vector in your vector space.  What this means is that basically you need to find an ordered list of vectors by ranking them, where the first row is the closest or most similar vector to the query vector. The second row in the list would be the second closest vector to the query vector, and so on, depending on your data set. What we need to do is to find the relative order of distances. And that's really what matters rather than the actual distance.  Now, similarity searches tend to get data from one or more clusters, depending on the value of the query vector and the fetch size. Approximate searches using vector indexes can limit the searches to specific clusters. Exact searches visit vectors across all clusters.  05:51 Lois: Let's talk about how we actually convert information into these vectors. There are models behind the scenes, right? Kind of like translators between words, images, and numbers. Brent, what embedding models does Oracle support, and how do they handle different data types? Brent: Vector embedding models allow you to assign meaning to what a word, or a sentence, or the pixels in an image, or perhaps audio. What that actually means? It allows you to quantify features or dimensions.  Most modern vector embeddings use a transformer model. Bear in mind that convolutional neural networks can also be used. Depending on the type of your data, you can use different pretrained open-source models to create vector embeddings. As an example, for textual data, sentence transformers can transform words, sentences, or paragraphs into vector embeddings.  For visual data, you can use residual network, also known as ResNet, to generate vector embeddings. You can also use visual spectrogram representation for audio data. And that allows us to use the audio data to fall back into the visual data case. Now, these can also be based on your own data set. Each model also determines the number of dimensions for your vectors.  As an example, Cohere's embedding model, embed English version 3.0, has 1,024 dimensions. Open AI's embedding model, text-embedding-3-large, has 3,072 dimensions.  07:45 Nikita: For organizations ready to put this into practice, there's the question of how to get the models up and running inside Oracle Database. Can you walk us through how these models are brought into Oracle Database? Brent: Although you can generate vector embeddings outside the Oracle Database using pre-trained open-source embeddings or your own embedding models, you also have the option of doing those within the Oracle Database. In order to use those within the Oracle Database, you need to use models that are compatible with the Open Neural Network Exchange Standard, or ONNX, also known as onn-ex.  Oracle Database implements an ONNX runtime directly within the database, and this is going to allow you to generate vector embeddings directly inside the Oracle Database using SQL.  08:41 AI is transforming every industry. So, it's no wonder that AI skills are the most sought-after by employers. If you're ready to dive into AI, check out the OCI AI Foundations training and certification that's available for free! It's the perfect starting point to build your AI knowledge. Head over to mylearn.oracle.com to kickstart your AI journey today! 09:06 Nikita: Welcome back! Let's make this practical. Imagine I'm setting this up for the first time. What are the big steps? Can you walk us through the end-to-end workflow using Oracle AI Vector Search? Brent: Generate vector embeddings from your data, either outside the database or within the database. Now, embeddings are a mathematical representation of what your data meaning is. So, what does this long sentence mean, for instance? What are the main keywords out of it? You can also generate embeddings not only on your typical string type of data, but you can also generate embeddings on other types of data, such as pictures or perhaps maybe audio wavelengths.  Maybe we want to convert text strings to embeddings or convert files into text. And then from text, maybe we can chunk that up into smaller chunks and then generate embeddings on those chunks. Maybe we want to convert files to embeddings, or maybe we want to use embeddings for end-to-end search.  Now you have to generate vector embeddings from your unstructured data, as we mentioned, either outside or within the Oracle Database. You can either use the ONNX embedding machine learning models or you can access third-party REST APIs.  You can import pretrained models in ONNX format for vector generation within the database. You can download pretrained embedding machine learning models, convert them into the ONNX format if they are not already in that format. Then you can import those models into the Oracle Database and generate vector embeddings from your data within the database.  Oracle also allows you to convert pre-trained models to the ONNX format using Oracle machine learning for Python. This enables the use of text transformers from different companies.  11:36 Nikita: Once those embeddings are generated, what's the next step?  Brent: Store vector embeddings. So you can create one or more columns of the vector data type in your standard relational data tables. You can also store those in secondary tables that are related to the primary tables using primary key foreign key relationships.  You can store vector embeddings on structured data and relational business data in the Oracle Database. You do store the resulting vector embeddings and associated unstructured data with your relational business data inside the Oracle Database.  12:17 Lois: And when do vector indexes come into play?  Brent: Now you may want to create vector indexes in the event that you have huge vector spaces. This is an optional step, but this is beneficial for running similarity searches over those huge vector spaces.  12:38 Nikita: Now, once all of that is in place, how do users perform similarity searches?  Brent: So once you have generated the vector embeddings and stored those vector embeddings and possibly created the vector indexes, you can then query your data with similarity searches. This allows for native SQL operations and allows you to combine similarity searches with relational searches in order to retrieve relevant data.  So let's take a look at the combined complete workflow. Step number one, generate the vector embeddings from your unstructured data. Step number two, store the vector embeddings. Step number three, create vector indexes. And step number four, combine similarity and keyword searches.  Now there is another optional step. You could generate a prompt and send it to a large language model for a full RAG inference. You can use the similarity search results to generate a prompt and send it to your generative large language model in order to complete your RAG pipeline.  14:07 Lois: Thanks for that detailed walk-through, Brent. To sum up, today we introduced Oracle AI Vector Search, discussed its core concepts, data types, embedding models, and the complete workflow you'll use to get real value out of your business data, securely and efficiently.  Nikita: If you want to learn more about the topics we discussed today, go to mylearn.oracle.com and search for the Oracle AI Vector Search Fundamentals course. And if you're feeling inspired to try this out for yourself, don't forget to check out the Oracle Database 23ai SQL Workshop for hands-on training. Until next time, this is Nikita Abraham… Lois: And Lois Houston, signing off! 14:49 That's all for this episode of the Oracle University Podcast. If you enjoyed listening, please click Subscribe to get all the latest episodes. We'd also love it if you would take a moment to rate and review us on your podcast app. See you again on the next episode of the Oracle University Podcast.  

В этом выпуске мы вместе с Даулетом Жангузиным - инженером из Кремниевой долины с 15-летним опытом (NVIDIA, Groq, Cohere, Lyft, Google, Microsoft) - говорим о карьере в BigTech и о том, что происходит под капотом современного AI. Обсуждаем практичную сторону работы с большими моделями: как выжимать максимум из Nvidia GPU, чем полезен Claude в реальных задачах, и какие курсы/ресурсы действительно помогают расти инженеру, как пишут код в 2026 лучшие программисты Кремниевой Долине. Эпизод будет интересен тем, кто строит карьеру в разработке/ML, хочет понять трек BigTech (Microsoft → Google → Lyft), интересуется LLM-инфраструктурой и оптимизацией вычислений, а также ищет советы по обучению и прохождению технических собеседований в ведущие tech-компании.  Арман Сулейменов: https://www.instagram.com/armansu/ Даулет Жангузин: https://www.instagram.com/daulet/ Продюсер и режиссёр: Данияр Ахметжанов: https://www.instagram.com/good.years/ Наш Instagram: https://www.instagram.com/nfactorialpodcast/ Получите одну из самых востребованных профессий в мире - ИИ-разработчик - вместе с nFactorial School - https://www.nfactorial.school/courses_new/llm-engineer

The guys wasted no time in bringing you their special podcast recorded from the show floor in Barcelona. Overwhelming demand means they're delighted welcome no less than four guests, but are slightly more organised about it than last year. First they're joined by Ray Dolan from Cohere and Mike Dano from Ookla. Both are familiar faces but there was still plenty to catch up on, including talk of satellites and network sensing. Eventually they give way to Queen of Props, Totogi's Danielle Rios, and industry expert Dan Warren. Among the topics the four then discuss are network software and, of course, AI.

Ed Elson speaks with Nick Frosst, a co-founder of Cohere. They discuss why the company chose an enterprise-only strategy, how he sees the future of AI unfolding, and whether an IPO is on the horizon. Learn more about your ad choices. Visit podcastchoices.com/adchoices

Bell will become Cohere's “preferred Canadian AI infrastructure provider. Cohere touts homegrown roots but leans on US hardware and partners.  Lori Wilson reads Cohere Is Canada's Biggest AI Hope. Why Is It So American? by Julie Sobowale.   About AMIAMI is a not-for-profit media company that entertains, informs and empowers Canadians who are blind or partially sighted. Operating three broadcast services, AMI-tv and AMI-audio in English and AMI-télé in French, AMI's vision is to establish and support a voice for Canadians with disabilities, representing their interests, concerns and values through inclusion, representation, accessible media, reflection, representation and portrayal.Find more great AMI Original Content on AMI+Learn more at AMI.caConnect with Accessible Media Inc. online:X /Twitter @AccessibleMediaInstagram @AccessibleMediaInc / @AMI-audioFacebook at @AccessibleMediaIncTikTok @AccessibleMediaIncEmail feedback@ami.ca Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Send a textInvest in pre-IPO stocks with AG Dillon & Co. Contact aaron.dillon@agdillon.com to learn more. Financial advisors only. www.agdillon.com00:00 - Intro00:02 - AG Dillon Funds closing on Mar 31, 202600:51 - OpenAI Financials $280B revenue target meets $665B cost wall03:58 - OpenAI “buys” OpenClaw, Steinberger joins OpenAI04:42 - OpenAI Series C aims to shatter records at $850B post money05:41 - OpenAI and Tata bet on India with a 100 MW to 1 GW buildout path06:29 - Grafana's $9B round talks ride a $400M ARR wave07:23 - World Labs lands Autodesk and targets a rumored $5B valuation08:18 - Temporal wants to be the load bearing layer for agent execution09:31 - Mesh Optical's $50M Series A targets the chokepoint inside AI data centers10:43 - Render's $1.5B valuation is a bet that AI apps need a new runtime11:40 - Stash acquired by Grab for $425M13:06 - Physical Superintelligence pitches a physics breakthrough factory with a 20 person team14:07 - Figma plugs Claude Code into design and risks losing the workflow15:00 - Anthropic ships Sonnet 4.6 just 12 days after Opus 4.615:26 - Stripe's Bridge wins OCC trust charter signal as stablecoin scrutiny rises16:37 - Cohere puts 70 plus languages on device with a 3.35B parameter model17:53 - ElevenLabs turns agent risk into an insurable product at $12.2B secondary19:05 - Mistral buys Koyeb and adds 16 engineers to harden its compute stack

This episode is sponsored by tastytrade.  Trade stocks, options, futures, and crypto in one platform with low commissions and zero commission on stocks and crypto. Built for traders who think in probabilities, tastytrade offers advanced analytics, risk tools, and an AI-powered Search feature.   Learn more at https://tastytrade.com/ In this episode of Eye on AI, Nick Frosst, Co-Founder of Cohere and former Google Brain researcher, explains why Cohere is betting on enterprise AI instead of chasing AGI.   While much of the AI industry is focused on artificial general intelligence, Cohere is building practical, capital-efficient large language models designed for real-world enterprise deployment. Nick breaks down why scaling transformers does not equal AGI, why inference cost and ROI matter, and how enterprise AI differs from consumer AI hype.   We discuss enterprise LLM deployment, private data, regulated industries like banking and healthcare, agentic systems, evaluation benchmarks, and why AI will likely become embedded infrastructure rather than a headline breakthrough.   If you care about enterprise AI, AGI debates, large language models, and the future of AI in business, this conversation delivers a grounded perspective from inside one of the leading AI companies.   Stay Updated: Craig Smith on X: https://x.com/craigss Eye on A.I. on X: https://x.com/EyeOn_AI   (00:00) From Google Brain to Cohere (03:54) Discovering Transformers (06:39) The Transformer Dominance (09:44) What AGI Actually Means (12:26) Planes vs Birds: The AI Analogy (14:08) Why Cohere Isn't Chasing AGI (18:38) Distillation & Model Efficiency (21:42) What Enterprise AI Really Does (25:20) Private Data & Secure Deployment (26:59) Enterprise Use Cases (RBC Example) (32:22) Why AI Benchmarks Mislead (34:55) Why Most AI Stays in Demo (38:23) What "Agents" Actually Are (43:32) The Problem With AGI Fear (49:15) Scaling Enterprise AI (53:24) Why AI Will Get "Boring"  

Anthropic raises the second largest financing round of all time. Other AI players are beginning to show hockey stick revenue growth. Meta wants to add facial recognition to its glasses. Ring pulls back from some recognition partnerships for its camera. And, of course, your Weekend Longreads Suggestions. Anthropic closes $30 billion funding round as cash keeps flowing into top AI startups (CNBC) Enterprise AI startup Cohere tops revenue target as momentum builds to IPO: Investor memo (CNBC) Meta Plans to Add Facial Recognition Technology to Its Smart Glasses (NYTimes) Ring cancels its partnership with Flock Safety after surveillance backlash (The Verge) Weekend Longreads Suggestions: The AI Gold Rush Is Breaking a Silicon Valley Taboo: Cashing Out Before the IPO (WSJ) The New Fabio Is Claude (NYTimes) Learn more about your ad choices. Visit megaphone.fm/adchoices

Do startup valuations today make sense?Umesh Padval, an early investor in Cohere, now valued at about $7 billion shares why Cohere stood out at the time of his investment. He shares what he saw early that made him believe this was not just another AI model company.Umesh is the Founding Managing Partner, Seligman Ventures and previously at Thomvest and Bessemer Venture Partners. He brings experience from investing across multiple tech cycles, from chips to cloud to AI. Umesh talks about how deals are really done in venture capital and what he looks for when everything feels noisy and crowded in AI.He also shares why many strong companies are choosing to stay private and what has changed in the IPO market. Public markets now demand cash flow and durability, not just fast growth.Umesh talks about why open source has become a powerful sales funnel for modern AI companies. Developers become the first users, and community adoption turns into long-term enterprise revenue.After four decades in Silicon Valley and 20 years as a VC, Umesh shares what keeps him in building and investing.0:00 – How big is the scope for investing in AI startups?04:04 – Do unit economics justify large AI valuations?06:00 – Thomvest's LLM investment thesis (Cohere case study)09:18 – Are CTO roles changing in AI11:21 – Traits of the best AI founding teams13:40 – Timeline to find the best founders16:52 – Partnership with Jyoti Bansal19:07 – Where is the IPO market headed?23:40 – Salesforce–Clari acquisition25:18 – Is profitability a prerequisite to go public?26:00 – Can the India–US corridor beat US–Israel?28:53 – Umesh's investment philosophy31:08 – Open source as a sales funnel33:38 – IIT → Stanford → Startups41:45 – The only CEO with 60 direct reports43:43 – Why Jensen never does 1-on-1s?48:23 – What ultimately drives Umesh Padval?-------------India's talent has built the world's tech—now it's time to lead it.This mission goes beyond startups. It's about shifting the center of gravity in global tech to include the brilliance rising from India.What is Neon Fund?We invest in seed and early-stage founders from India and the diaspora building world-class Enterprise AI companies. We bring capital, conviction, and a community that's done it before.Subscribe for real founder stories, investor perspectives, economist breakdowns, and a behind-the-scenes look at how we're doing it all at Neon.-------------Check us out on:Website: https://neon.fund/Instagram: https://www.instagram.com/theneonshoww/LinkedIn: https://www.linkedin.com/company/beneon/Twitter: https://x.com/TheNeonShowwConnect with Siddhartha on:LinkedIn: https://www.linkedin.com/in/siddharthaahluwalia/Twitter: https://x.com/siddharthaa7-------------This video is for informational purposes only. The views expressed are those of the individuals quoted and do not constitute professional advice.Send a text

In this episode of Tank Talks, Matt Cohen and John Ruffolo rip through a stacked rundown of tech, venture capital, and geopolitical “sovereignty” theater. They open with Europe's accelerating shift away from Microsoft Office and big U.S. platforms toward open-source alternatives, then jump straight into a breaking change from Y Combinator CEO Garry Tan: Canada is back on the list of accepted incorporations, reversing a move that sparked serious backlash about Canadian startup brain drain and U.S.-domicile pressure.From there, they dissect Elon Musk's headline-grabbing SpaceX–xAI all-stock merger and why it looks way better for xAI holders than SpaceX shareholders ahead of a rumored SpaceX IPO window. The episode also digs into Canada's national AI consultation (and the government openly using multiple LLM providers like Cohere and OpenAI to process submissions), the EU's push for digital sovereignty (and the risks of swapping to “free” tools), and the brutal reality of AI-driven search gutting legacy media traffic, with the Washington Post laying off a third of its newsroom. The big throughline: information is cheap now, execution and trust are expensive, and countries (and companies) that don't adapt are about to get cooked.Y Combinator Reverses Course: Canada Back on the List (00:43)YC CEO Garry Tan adds Canada back to YC's list of accepted incorporation jurisdictions after removing it, triggering a wave of criticism. Matt and John break down what changed, why the original rationale (Canadian winners re-domiciling to the U.S.) was a flawed signal, and why the real issue is still Canadian capital formation and follow-on funding strength.SpaceX Buys xAI: A $1.25T Story Swap Before an IPO? (02:34)Matt tees up the shocker: SpaceX acquires xAI in an all-stock deal valuing xAI at $250B and SpaceX at $1T, creating a combined $1.25T entity. They discuss xAI's massive burn versus SpaceX's improving cash profile (driven by Starlink) and why this kind of move raises eyebrows heading into an IPO narrative.Second-Order Effects: When a Cash-Burning AI Company Merges Into Space Infrastructure (07:35)They debate whether this becomes a template for other pre-IPO restructures or stays a one-off “Elon special.” John says a Starlink-style consolidation would make strategic sense; folding in xAI doesn't feel like a choke-point win.Canada's AI Strategy Consultation: Government Using LLMs in the Workflow (09:10)Canada's ISED publishes a high-level summary of its AI consultation and explicitly notes using multiple LLMs and pipelines (including Cohere and OpenAI) to process massive public input. Matt frames this as a meaningful “government actually doing something” moment, even if the public is still anxious about jobs and privacy.Europe's Digital Sovereignty Push: Dropping Teams/Zoom for Open Source? (12:40)They react to reports of governments moving away from Teams/Zoom and Microsoft tooling in the name of sovereignty. Matt calls the open-source swap risky from a security and operational standpoint; John says the bigger signal is global: sovereignty is now a first-order priority, and Canada can't pretend this wave isn't coming.Washington Post Layoffs: AI Search Is Eating the Referral Economy (16:48)Matt highlights the Washington Post's reported search traffic collapse and layoffs impacting a third of the newsroom. John calls journalism an obvious early disruption target: LLMs compress content production costs, and the old newsroom pyramid doesn't match the new economics.The Survival Play: Media Becomes a Live Events Business (19:26)They land on the counter-move: stop fighting the trend and monetize what still works: brand, access, community, and in-person experiences. If content becomes commoditized, relationships and trust become the product.Connect with John Ruffolo on LinkedIn: https://ca.linkedin.com/in/joruffoloConnect with Matt Cohen on LinkedIn: https://ca.linkedin.com/in/matt-cohen1Visit the Ripple Ventures website: https://www.rippleventures.com/ This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit tanktalks.substack.com

Joelle Pineau is the chief AI officer at Cohere. Pineau joins Big Technology Podcast to discuss where the cutting edge of AI research is headed — and what it will take to move from impressive demos to reliable agents. Tune in to hear why memory, world models, and more efficient reasoning are emerging as the next big frontiers, plus what current approaches are missing. We also cover the “capability overhang” in enterprise AI, why consumer assistants still aren't lighting the world on fire, what AI sovereignty actually means, and whether the major labs can ever pull away from each other. Hit play for a cool-headed, deeply practical look at what's next for AI and how it gets deployed in the real world. --- Enjoying Big Technology Podcast? Please rate us five stars ⭐⭐⭐⭐⭐ in your podcast app of choice. Want a discount for Big Technology on Substack + Discord? Here's 25% off for the first year: https://www.bigtechnology.com/subscribe?coupon=0843016b Learn more about your ad choices. Visit megaphone.fm/adchoices

Foreign aid budgets have been slashed significantly by governments in the United States, Europe, and beyond, raising questions about what humanitarian assistance will look like in practice. Recent and abrupt funding cuts by major donors are already affecting refugee-hosting countries, where resources were strained even before these changes. In this episode of World of Migration, host Lawrence Huang speaks with Micheal Gumisiriza, a program lead based in southwest Uganda for COHERE, an international NGO that works with refugee-led organizations, about how funding cuts by international donors are being felt on the ground—from food assistance and access to essential medicines to education. They discuss what the immediate impacts reveal about the humanitarian system's capacity under pressure, and what “localization” could realistically mean as humanitarian response efforts adjust to a period of shrinking resources.  

We are reupping this episode after LMArena announced their fresh Series A (https://www.theinformation.com/articles/ai-evaluation-startup-lmarena-valued-1-7-billion-new-funding-round?rc=luxwz4), raising $150m at a $1.7B valuation, with $30M annualized consumption revenue (aka $2.5m MRR) after their September evals product launch.—-From building LMArena in a Berkeley basement to raising $100M and becoming the de facto leaderboard for frontier AI, Anastasios Angelopoulos returns to Latent Space to recap 2025 in one of the most influential platforms in AI—trusted by millions of users, every major lab, and the entire industry to answer one question: which model is actually best for real-world use cases? We caught up with Anastasios live at NeurIPS 2025 to dig into the origin story (spoiler: it started as an academic project incubated by Anjney Midha at a16z, who formed an entity and gave grants before they even committed to starting a company), why they decided to spin out instead of staying academic or nonprofit (the only way to scale was to build a company), how they're spending that $100M (inference costs, React migration off Gradio, and hiring world-class talent across ML, product, and go-to-market), the leaderboard delusion controversy and why their response demolished the paper's claims (factual errors, misrepresentation of open vs. closed source sampling, and ignoring the transparency of preview testing that the community loves), why platform integrity comes first (the public leaderboard is a charity, not a pay-to-play system—models can't pay to get on, can't pay to get off, and scores reflect millions of real votes), how they're expanding into occupational verticals (medicine, legal, finance, creative marketing) and multimodal arenas (video coming soon), why consumer retention is earned every single day (sign-in and persistent history were the unlock, but users are fickle and can leave at any moment), and his vision for Arena as the central evaluation platform that provides the North Star for the industry—constantly fresh, immune to overfitting, and grounded in millions of real-world conversations from real users.We discuss:* The $100M raise: use of funds is primarily inference costs (funding free usage for tens of millions of monthly conversations), React migration off Gradio (custom loading icons, better developer hiring, more flexibility), and hiring world-class talent* The scale: 250M+ conversations on the platform, tens of millions per month, 25% of users do software for a living, and half of users are now logged in* The leaderboard illusion controversy: Cohere researchers claimed undisclosed private testing created inequities, but Arena's response demolished the paper's factual errors (misrepresented open vs. closed source sampling, ignored transparency of preview testing that the community loves)* Why preview testing is loved by the community: secret codenames (Gemini Nano Banana, named after PM Naina's nickname), early access to unreleased models, and the thrill of being first to vote on frontier capabilities* The Nano Banana moment: changed Google's market share overnight, billions of dollars in stock movement, and validated that multimodal models (image generation, video) are economically critical for marketing, design, and AI-for-science* New categories: occupational and expert arenas (medicine, legal, finance, creative marketing), Code Arena, and video arena coming soonFull Video EpisodeTimestamps00:00:00 Introduction: Anastasios from Arena and the LM Arena Journey00:01:36 The Anjney Midha Incubation: From Berkeley Basement to Startup00:02:47 The Decision to Start a Company: Scaling Beyond Academia00:03:38 The $100M Raise: Use of Funds and Platform Economics00:05:10 Arena's User Base: 5M+ Users and Diverse Demographics00:06:02 The Competitive Landscape: Artificial Analysis, AI.xyz, and Arena's Differentiation00:08:12 Educational Value and Learning from the Community00:08:41 Technical Migration: From Gradio to React and Platform Evolution00:10:18 Leaderboard Delusion Paper: Addressing Critiques and Maintaining Integrity00:12:29 Nano Banana Moment: How Preview Models Create Market Impact00:13:41 Multimodal AI and Image Generation: From Skepticism to Economic Value00:15:37 Core Principles: Platform Integrity and the Public Leaderboard as Charity00:18:29 Future Roadmap: Expert Categories, Multimodal, Video, and Occupational Verticals00:19:10 API Strategy and Focus: Doing One Thing Well00:19:51 Community Management and Retention: Sign-In, History, and Daily Value00:22:21 Partnerships and Agent Evaluation: From Devon to Full-Featured Harnesses00:21:49 Hiring and Building a High-Performance Team Get full access to Latent.Space at www.latent.space/subscribe

From building LMArena in a Berkeley basement to raising $100M and becoming the de facto leaderboard for frontier AI, Anastasios Angelopoulos returns to Latent Space to recap 2025 in one of the most influential platforms in AI—trusted by millions of users, every major lab, and the entire industry to answer one question: which model is actually best for real-world use cases? We caught up with Anastasios live at NeurIPS 2025 to dig into the origin story (spoiler: it started as an academic project incubated by Anjney Midha at a16z, who formed an entity and gave grants before they even committed to starting a company), why they decided to spin out instead of staying academic or nonprofit (the only way to scale was to build a company), how they're spending that $100M (inference costs, React migration off Gradio, and hiring world-class talent across ML, product, and go-to-market), the leaderboard delusion controversy and why their response demolished the paper's claims (factual errors, misrepresentation of open vs. closed source sampling, and ignoring the transparency of preview testing that the community loves), why platform integrity comes first (the public leaderboard is a charity, not a pay-to-play system—models can't pay to get on, can't pay to get off, and scores reflect millions of real votes), how they're expanding into occupational verticals (medicine, legal, finance, creative marketing) and multimodal arenas (video coming soon), why consumer retention is earned every single day (sign-in and persistent history were the unlock, but users are fickle and can leave at any moment), the Gemini Nano Banana moment that changed Google's market share overnight (and why multimodal models are becoming economically critical for marketing, design, and AI-for-science), how they're thinking about agents and harnesses (Code Arena evaluates models, but maybe it should evaluate full agents like Devin), and his vision for Arena as the central evaluation platform that provides the North Star for the industry—constantly fresh, immune to overfitting, and grounded in millions of real-world conversations from real users. We discuss: The $100M raise: use of funds is primarily inference costs (funding free usage for tens of millions of monthly conversations), React migration off Gradio (custom loading icons, better developer hiring, more flexibility), and hiring world-class talent The scale: 250M+ conversations on the platform, tens of millions per month, 25% of users do software for a living, and half of users are now logged in The leaderboard illusion controversy: Cohere researchers claimed undisclosed private testing created inequities, but Arena's response demolished the paper's factual errors (misrepresented open vs. closed source sampling, ignored transparency of preview testing that the community loves) Why preview testing is loved by the community: secret codenames (Gemini Nano Banana, named after PM Naina's nickname), early access to unreleased models, and the thrill of being first to vote on frontier capabilities The Nano Banana moment: changed Google's market share overnight, billions of dollars in stock movement, and validated that multimodal models (image generation, video) are economically critical for marketing, design, and AI-for-science New categories: occupational and expert arenas (medicine, legal, finance, creative marketing), Code Arena, and video arena coming soon Consumer retention: sign-in and persistent history were the unlock, but users are fickle and earned every single day—"every user is earned, they can leave at any moment" — Anastasios Angelopoulos Arena: https://lmarena.ai X: https://x.com/arena Chapters 00:00:00 Introduction: Anastasios from Arena and the LM Arena Journey 00:01:36 The Anjney Midha Incubation: From Berkeley Basement to Startup 00:02:47 The Decision to Start a Company: Scaling Beyond Academia 00:03:38 The $100M Raise: Use of Funds and Platform Economics 00:05:10 Arena's User Base: 5M+ Users and Diverse Demographics 00:06:02 The Competitive Landscape: Artificial Analysis, AI.xyz, and Arena's Differentiation 00:08:12 Educational Value and Learning from the Community 00:08:41 Technical Migration: From Gradio to React and Platform Evolution 00:10:18 Leaderboard Delusion Paper: Addressing Critiques and Maintaining Integrity 00:12:29 Nano Banana Moment: How Preview Models Create Market Impact 00:13:41 Multimodal AI and Image Generation: From Skepticism to Economic Value 00:15:37 Core Principles: Platform Integrity and the Public Leaderboard as Charity 00:18:29 Future Roadmap: Expert Categories, Multimodal, Video, and Occupational Verticals 00:19:10 API Strategy and Focus: Doing One Thing Well 00:19:51 Community Management and Retention: Sign-In, History, and Daily Value 00:22:21 Partnerships and Agent Evaluation: From Devon to Full-Featured Harnesses 00:21:49 Hiring and Building a High-Performance Team

We review four clips from the Dwarkesh Patel Podcast with Satya Nadella, Microsoft's CEO. I highly recommend Dwarkesh's show—technical & nerdy, but excellent.Satya talks about scaffolding—the software wrapped around AI models to make them actually work.So we speak with someone building that scaffolding: Neil McKechnie runs two AI-first startups as a CTO. He discusses how he orchestrates up to twelve different language models—GPT-5, Claude, Gemini, Llama, Mistral, Cohere, Perplexity. We discuss what it actually takes to build production systems with LLMs today—and what that reveals about the agent future we're being pitched.Dwarkesh's Podcast:https://www.youtube.com/@DwarkeshPatelTo stay in touch, sign up for our newsletter at https://www.superprompt.fm

Each week, the leading journalists in legal tech choose their top stories of the week to discuss with our other panelists.   This week's topics: 00:00 Introductions 03:26 From 'Who Luck' to 'Who's Here?': The TLTF Summit Continues to Excel, Even As It Expands (Selected by Bob Ambrogi) 20:36 Why "AI Essentials" Still Matter — Even for the Smartest People in the Room (Selected by Stephanie Wilkins) 21:25 Discussion on AI expectations, in-house vs. law-firm dynamics   (Related to Rhys Dipshan's TLTF Summit Takeaways story) 25:48 McDermott acknowledges 'fielding inbound interest' from outside investors as it listens to new ideas (Selected by Caroline Hill / Victor Li) 31:11 Discussion on MSOs, private equity influence, and law-firm structural changes   (Related to Rhys Dipshan's TLTF Summit Takeaways story) 51:47 Cohere is Canada's Biggest AI Hope. Why is it so American? (Selected by Julie Sobowale)

פרק מספר 505 של רברס עם פלטפורמה - באמפרס מספר 89, שהוקלט ב-13 בנובמבר 2025, רגע אחרי כנס רברסים 2025 [יש וידאו!]: רן, דותן ואלון (והופעת אורח של שלומי נוח!) באולפן הוירטואלי עם סדרה של קצרצרים מרחבי האינטרנט: הבלוגים, ה-GitHub-ים, ה-Claude-ים וה-GPT-ים החדשים מהתקופה האחרונה.

Is Canada's A.I. champion eating the news?Canada is betting big on Cohere, but a lawsuit alleges that the company's flagship LLM is bypassing paywalls and hallucinating content. What happens to the news industry if A.I. continues to run amok? Host: Jesse BrownCredits: James Nicholson (Producer), Jules Bugiel (Associate Producer and Fact Checking) Caleb Thompson (Audio Editor and Technical Producer), max collins (Director of Audio), Jesse Brown (Editor)Guest: Douglas SoltysAdditional music by Audio NetworkFurther Reading on Our Website Sponsors: Sprague Cannery: You can find Sprague goods across the nation in major Canadian retailers like Costco, Loblaws, Walmart, Giant Tiger and many smaller independent stores.Douglas: Douglas is giving our listeners a FREE Sleep Bundle with each mattress purchase. Get the sheets, pillows, mattress and pillow protectors FREE with your Douglas purchase today. Visit douglas.ca/canadaland to claim this offer.Squarespace: Check out Squarespace.com/canadaland for a free trial, and when you're ready to launch use code canadaland to save 10% off your first purchase of a website or domain.If you value this podcast, Support us! You'll get premium access to all our shows ad free, including early releases and bonus content. You'll also get our exclusive newsletter, discounts on merch at our store, tickets to our live and virtual events, and more than anything, you'll be a part of the solution to Canada's journalism crisis, you'll be keeping our work free and accessible to everybody. Hosted on Acast. See acast.com/privacy for more information.

How do companies like Salesforce and Dell scale intelligence across every cloud?Aidan Gomez, co-founder and CEO of Cohere, explains how they're building AI that works across all enterprise systems and deploys anywhere, giving companies true flexibility and security.He joins Joubin Mirzadegan for a wide-ranging conversation on why synthetic data went from dismissed to indispensable, and how the race among AI labs is really unfolding.Guest: Aidan Gomez, co-founder and CEO of CohereConnect with Aidan: XLinkedInConnect with Joubin: XLinkedInEmail: grit@kleinerperkins.comLearn more about Kleiner Perkins

Joelle Pineau is the Chief Scientist at Cohere, where she leads research on advancing large language models and practical AI systems. Before joining Cohere, she was VP of AI Research at Meta, where she founded and led Meta AI's Montreal lab. A professor at McGill University, Joelle is renowned for her pioneering work in reinforcement learning, robotics, and responsible AI development. AGENDA:  00:00 Introduction to AI Scaling Laws 03:00 How Meta Shaped How I Think About AI Research 04:36 Challenges in Reinforcement Learning 10:00 Is It Possible to be Capital Efficient in AI 15:52 AI in Enterprise: Efficiency and Adoption 22:15 Security Concerns with AI Agents 28:34 Can Zuck Win By Buying the Galacticos of AI 32:15 The Rising Cost of Data 35:28 Synthetic Data and Model Degradation 37:22 Why AI Coding is Akin to Image Generation in 2015 48:46 If Joelle Was a VC Where Would She Invest? 52:17 Quickfire: Lessons from Zuck, Biggest Mindset Shift  

In this episode of Tank Talks, host Matt Cohen and John Ruffolo break down the most important stories shaping Canada's innovation economy, from the upcoming federal budget and its impact on founders and investors, to Canada's fintech shake-up as open banking finally gains momentum.The duo dives into AI's growing legal minefield, including the mounting lawsuits against Perplexity and Sora, and discusses what this means for startups training models on licensed versus unlicensed data. They also unpack Cohere's rumored IPO, Canada's AI partnership with the UAE, and what it reveals about the country's global strategy for data centers and sovereign capital.From Blue Jays playoff economics to AI data sovereignty, this Rundown is packed with sharp insights, timely analysis, and the kind of candid commentary you won't hear anywhere else.A Quick Word from our Sponsor, FaskenAt Fasken, our clients don't wait for the future. They build it. As the first and largest dedicated emerging tech practice in Canada, our team is composed of founders, ex in-house counsel, developers and business advisors who have guided clients from startup, to scale-up, to exit. The trust of our clients has enabled us to consistently rank at the top of every major Canadian M&A, Capital Markets and Venture Capital league table. With deep industry knowledge and experience across all areas of emerging and high growth technology including ClimateTech, MedTech, Artificial Intelligence, Fintech, and AgTech we're your partners within the innovation ecosystem as you transform the landscape of what's possible.Tomorrow starts here. Own it with us.For more information, visit fasken.com/emergingtech and follow us on LinkedIn.Canada's Make-or-Break Federal Budget (08:46)With the federal budget weeks away, John calls this the Liberal government's credibility test, a defining moment for innovation, R&D reform, and fiscal discipline.* The state of Canada's finances and investor sentiment* Expectations for R&D tax credit and AI policy reform* Why “good ideas” might not matter if the fiscal hole is too deepOpen Banking Finally Gets Real (12:55)The Bank of Canada registers 300 new payment service providers, marking a major milestone for Canada's fintech ecosystem.* How this could shake up the Big 5 banks' oligopoly* Why Wealthsimple, Shopify, and Koho stand to gain* John's take on trust, liquidity, and the future of financial competitionCanada-UAE AI Investment Deal (15:34)AI Minister Evan Solomon signs a non-binding MOU with the UAE on data center investment. Is this a real opportunity or political theater?* What “non-binding” really means for Canada's capital strategy* Mark Carney's push to diversify trade away from the U.S.* Why every major country is chasing sovereign data capitalCohere's IPO Tease and the AI Hype Cycle (18:11)Cohere's CEO Aidan Gomez hints at “going public soon.” Matt and John weigh the risks and timing of an AI IPO in a frothy market.* Lessons from the Faire America IPO and $16B valuations with no assets* The pressure of capital requirements in AI infrastructure* Why timing the public markets almost never worksAI Lawsuits, IP Infringement, and Data Licensing Wars (20:48)From Reddit vs Perplexity to Hollywood vs. Sora, Matt and John break down the growing AI legal battles over content rights.* The global IP divide: what happens when China ignores licensing rules* Why only the biggest players can afford compliance* The coming “Rule of Three” in the AI data economyConnect with John Ruffolo on LinkedIn: https://ca.linkedin.com/in/joruffoloConnect with Matt Cohen on LinkedIn: https://ca.linkedin.com/in/matt-cohen1Visit the Ripple Ventures website: https://www.rippleventures.com/ This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit tanktalks.substack.com

Its official Cohere has just hit a $7B valuation a month after its last raise and it partners with AMD.Get the top 40+ AI Models for $20 at AI Box: ⁠⁠https://aibox.aiAI Chat YouTube Channel: https://www.youtube.com/@JaedenSchaferJoin my AI Hustle Community: https://www.skool.com/aihustle

Jay Alammar is Director and Engineering Fellow at Cohere and co-author of the O'Reilly book “Hands-on Large Language Models.” Subscribe to the Gradient Flow Newsletter

Nick Frosst is a Canadian AI researcher and entrepreneur, best known as co-founder of Cohere, the enterprise-focused LLM. Cohere has raised over $900 million, most recently a $500 million round, bringing its valuation to $6.8 billion. Under his leadership, Cohere hit $100M in ARR. Prior to founding Cohere, Nick was a researcher at Google Brain and a protégé of Geoffrey Hinton. AGENDA:  00:00 – Biggest lessons from Geoff Hinton at Google Brain? 02:10 – Did Google completely sleep at the wheel and miss ChatGPT? 05:45 – Is data or compute the real bottleneck in AI's future? 07:20 – Does GPT5 Prove That Scaling Laws are BS? 13:30 – Are AI benchmarks just total BS? 17:00 – Would Cohere spend $5M on a single AI researcher? 19:40 – What is nonsense in AI that everyone is talking about? 25:30 – What is no one talking about in AI that everyone should be talking about? 33:00 – How do Cohere compete with OpenAI and Anthropic's billions? 44:30 – Why does being American actually hurt tech companies today? 45:10 – Should countries fund their own models? Is model sovereignty the future? 52:00 – Why has Sam Altman actually done a disservice to AI?  

It's a Friday TWiST and Jason and Alex are FIRED UP about this internal Meta doc laying out appropriate vs. inappropriate AI behavior… You won't BELIEVE with what Zuck approves for 8-year-old users.PLUS… AI job displacement is HERE, at least in the Big Apple… Jason's getting kind of paranoid about the surveillance state… AI remains frothier than ever through new Cohere and Cognition rounds… and why we're dubious that Sam Altman REALLY plans to spend $1 trillion on OpenAI data centers.It's all happening on a brand-new This Week in Startups. Give it a click!Timestamps:(0:00) Intro - How Opendoor became a meme stock(06:58) When companies have less value than cash on hand… what gives?(10:23) - (11:24) Bolt - Don't be left behind. Build apps quickly without knowing how to code with Bolt.new. Try it free at https://www.bolt.new/twist(19:13) New York's not adding jobs… Jason and Alex dig in and offer some theories.(20:25) - (21:30) Northwest Registered Agent - Form your entire business identity in just 10 clicks and 10 minutes. Get more privacy, more options, and more done—visit https://www.northwestregisteredagent.com/twist today!(26:56) AI job displacement is HERE and it's happening, folks…(30:05) Why Jason predicts unemployment will be 20% higher 1 year from today… add it to the TWiST Calendar(30:24) - (31:37) Alphasense - Get deeper insights into your business with the power of AI search and market intelligence. Start with a free trial at https://www.alpha-sense.com/twist(33:02) Wait, Meta AI is having “sensual” chats with children? WHY?(40:00) Will there be fallout to the Meta exposé? Legal? Staffing? Otherwise?(47:47) AI remains frothier than ever with new Cohere and Cognition rounds.(52:33) Sam Altman says he's going to spend $1 trillion on data centers… Jason's dubious.Subscribe to the TWiST500 newsletter: https://ticker.thisweekinstartups.comCheck out the TWIST500: https://www.twist500.comSubscribe to This Week in Startups on Apple: https://rb.gy/v19fcpFollow Lon:X: https://x.com/lonsFollow Alex:X: https://x.com/alexLinkedIn: ⁠https://www.linkedin.com/in/alexwilhelmFollow Jason:X: https://twitter.com/JasonLinkedIn: https://www.linkedin.com/in/jasoncalacanisThank you to our partners:(10:23) Bolt - Don't be left behind. Build apps quickly without knowing how to code with Bolt.new. Try it free at https://www.bolt.new/twist(20:25) Northwest Registered Agent - Form your entire business identity in just 10 clicks and 10 minutes. Get more privacy, more options, and more done—visit https://www.northwestregisteredagent.com/twist today!(30:24) Alphasense - Get deeper insights into your business with the power of AI search and market intelligence. Start with a free trial at https://www.alpha-sense.com/twistGreat TWIST interviews: Will Guidara, Eoghan McCabe, Steve Huffman, Brian Chesky, Bob Moesta, Aaron Levie, Sophia Amoruso, Reid Hoffman, Frank Slootman, Billy McFarlandCheck out Jason's suite of newsletters: https://substack.com/@calacanisFollow TWiST:Twitter: https://twitter.com/TWiStartupsYouTube: https://www.youtube.com/thisweekinInstagram: https://www.instagram.com/thisweekinstartupsTikTok: https://www.tiktok.com/@thisweekinstartupsSubstack: https://twistartups.substack.comSubscribe to the Founder University Podcast: https://www.youtube.com/@founderuniversity1916

In this episode, I sit down with my best friend and fellow entrepreneur, Anette Oran, to talk about the bold and beautiful life she's built—across borders, time zones, and industries. Anette shares what it's been like to relocate to a brand new country with her husband, how she creates grounding rituals in unfamiliar places, and the unexpected gifts that come from living outside your comfort zone. We also dive into Cohere, the software company she co-founded to support coaches and course creators in building transformational programs with ease. Anette drops wisdom on staying rooted in your purpose, avoiding the comparison trap (especially in the digital world), and how to create a life that feels like you, wherever you are. Whether you're a digital nomad, an aspiring entrepreneur, or simply craving a fresh perspective on life, this episode is a warm, inspiring listen that will leave you feeling grounded and expansive all at once.