Podcasts about cohere

Days after the US government forced Anthropic to pull Mythos 5 and Fable 5 offline, Cohere Chief AI Officer Joelle Pineau joins Jason Howell to react. The former Meta FAIR leader makes the case for sovereign AI, models that enterprises run on their own terms, on-premise and under their control, and explains why the shutdown was a wake-up call for everyone depending on a single provider.Also in this conversation: why Cohere open-sourced its North Mini Code model, what stops an agent from taking an action it shouldn't, how AI is changing entry-level work, and where AI actually belongs in medicine and drug discovery.New episodes at aiinside.show. Note: Time codes subject to change depending on dynamic ad insertion by the distributor. CHAPTERS: 0:00 - Start 0:02:28 - Honorary doctorate from the University of Ottawa, uncertainty for new graduates 0:10:33 - Grads: Say Yes to the role they're not quite ready for 0:22:07 - Leaving Meta's Fundamental AI Research team 0:27:06 - North Mini Code 0:34:29 - History with AI in personalized medicine 0:43:21 - What does North do today that stops an agent from behaving badly? 0:45:51 - Cohere and sovereignty with the current dispute between Anthropic and the U.S. government 0:50:39 - Are we any closer to agents that can build connections to understand each other? Hosts: Jason Howell Guest: Joelle Pineau Download and subscribe to AI Inside in audio and video: https://aiinside.show/  Support the podcast on Patreon for special perks: https://www.patreon.com/aiinsideshow. You'll get ad-free episodes, members-only Discord, T-shirts and stickers you love, and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Learn more about your ad choices. Visit megaphone.fm/adchoices

Canada is getting its first legal prediction markets app as Wealthsimple teams up with Kalshi, blurring the line between investing and gambling. Then, Apple warns iPhones could be headed toward the $2,000 mark as AI-fueled chip costs spike. Plus, in The Big Picture: the U.S. and Iran sign a deal to end the war, Empire doubles down on discount grocers, and Cohere locks in more Canadian compute from Bell.The Peak Daily is produced in partnership with reframevid.com

The Canadian government is going all in on AI, without understanding the real effects of the technology. Hadrian Mertins-Kirkwood joins Paris Marx to discuss Mark Carney's push for large-scale investment in AI, despite being unable to describe how adoption will work, how Canadians will benefit, and what policies will be implemented to mitigate growing risks and harms. Hadrian Mertins-Kirkwood senior researcher and political economist at the Canadian Centre for Policy Alternatives.The podcast is made in partnership with The Nation. Production is by Kyla Hewson. Support the show on Patreon.Also mentioned in this episode:Hadrian wrote about the Canadian government's new AI strategy.Paris wrote about the AI strategy and the new social media policy.The Canada Revenue Agency chatbot is expensive and giving incorrect information.The Canadian Immigration Department is using AI that hallucinates when reviewing applications.AI tools intended for Ontario doctors are providing incorrect information.Here's an overview of the federal AI strategy, “AI for All.”Support the show

"If your entire technology is coming from a single country, and that country decides that every now and again they're going to shut off access to you, that's not a foundation you can build on." The US government just ordered Anthropic to ban access to its most advanced AI models, Fable 5 and Mythos 5. Seems like now is a good time to talk about sovereign AI. Cohere co-founder Nick Frosst joins to discuss how Canada's AI champion is built different than the other frontier LLM providers, how Star Trek informs the type of AI future the company is trying to create, and why he doesn't make a point of listening to Marc Andreessen about AGI. Did the Anthropic model ban prove Cohere is right about sovereign AI? Let's dig in. -- Amid global uncertainty, the path forward is clear: Canada's moment to build is now. Presented by Uber Canada, DMZ, and National Bank of Canada, BetaKit Most Ambitious is back, telling stories of nearly 100 Canadian innovators strengthening our nation's autonomy, security, and prosperity. Read BetaKit Most Ambitious now.

Hey folks, Alex here, and welcome to a BIG MODEL week! We finally got Mythos (well almost)! Let me catch you up! This week started with WWDC26 from Apple, and Max Weinbach, who was in the room at Apple Park and actually has access to some of the new features including an all new SIRI AI, joined us to break down what could be the most used AI in the world very soon. At first I was skeptical, but he convinced me that the new Siri is actually good! Then, we saw the ultimate model drop: Anthropic finally shipped Mythos (X, my system card thread, benchmarks). Same weights, two names: Mythos 5 is the unrestricted version that only Project Glasswing partners get, Fable 5 is what the rest of us get, wrapped in the heaviest guardrails I've ever seen ship on a frontier model. It's state of the art on nearly every benchmarkThe model that was “too dangerous to release” is now... well, released, but with the heaviest guardrails we've seen. More on this later. Peter Gostev from Arena.ai joined us to break down the new model. Last but definitely not least, Google released a real-time translation model, that our friend Thor Schaeff from DeepMind demoed live, while we all spoke in different languages and it translated us in REAL TIME. It was really cool, definitely check that out. There's quite a few more things, like Loop Engineering Alpha, Swyx came by to talk about FrontierCode, OpenAI confirmed our suspicions that the anti-datacenter social media posts could be a concerted effort by groupds links to the Chinese government and much more. Let's dive in! ThursdAI - Let me catch you up, every week!

Last week, Mark Carney announced his big A.I. strategy. Two billion dollars to support creating jobs, providing free A.I. literacy training, and protection against some of the potential harms and risks around A.I., especially with kids. Oh, and he wants to build a world-leading supercomputer. Canada's leading A.I. company is Cohere. Cohere isn't like OpenAI or xAI or Anthropic or any of those other well-known large language model companies. They're not public facing. They don't do image generation or music generation, or tell you what recipe you can make with the leftovers in your fridge. They develop private models for specific companies trained in part on that company's private data. RBC, Bell, Salesforce, just to name a few. Their current valuation sits at $7 billion. Cohere's co-founder is a 33 year-old indie rockstar named Nick Frosst. He joins host Stephen Marche to discuss Canada's A.I. sector, his band, Star Trek, and those students booing A.I. at convocations.Host: Stephen MarcheCredits: Tristan Capacchione (Producer), Bruce Thorson (Senior Producer), max collins (Director of Audio), Jesse Brown (Editor and Publisher)Fact checking by Julian AbrahamPhoto: Gabriel HutchinsonAdditional music by: Audio NetworkSponsors: Fizz: Visit https://fizz.ca and activate a first plan using the referral code CAN25 to get 40$ off and 10GB of free data.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 https://douglas.ca/canadaland to claim this offer.Shopify: Sign up for your one-dollar-per-month trial today at https://shopify.caArticle: Article is offering our listeners $50 off your first purchase of $100 or more. To claim, visit https://article.com/canadaland and the discount will be automatically applied at checkout.Can't get enough Canadaland? Follow @Canadaland_Podcasts on Instagram for clips, announcements, explainers and more.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.

Der CEO von Cohere spricht über den Wettbewerb mit Tech-Giganten, die Konzentration von Macht – und sein Praktikum, in dem die KI-Revolution begann.

Lors d'une discussion animée par Bruno Guglielminetti, Valérie Pisano (Mila) et Joëlle Pineau (Cohere) ont dressé un portrait lucide de l'écosystème canadien de l'IA. Les deux dirigeantes estiment que le Canada possède déjà les talents, la recherche et les entreprises nécessaires pour jouer un rôle mondial, mais qu'il doit maintenant accélérer la commercialisation de ses innovations. Elles ont aussi annoncé une collaboration entre Mila et Cohere afin de développer des modèles mieux adaptés à la langue, à la culture et aux réalités québécoises. L'entretien aborde également les enjeux de souveraineté numérique, de rétention des talents et de création d'alliances internationales face à la domination américaine et chinoise. Leur message est clair : le Canada ne manque plus d'expertise, il doit maintenant passer rapidement de la réflexion à l'action. Merci au Cercle canadien de Montréal pour cette collaboration.

L’IA va-t-elle éliminer des emplois par millions ou va-t-elle plutôt rendre les travailleurs superproductifs? En tout cas, elle aura un impact majeur sur l’économie mondiale et le marché du travail. Fabien Curto Millet, l’économiste en chef de Google, partage ses prévisions avec Pascal et Alain. Aussi: Oura Ring 5, la plus petite bague connectée sur le marché, et peut-être la plus sophistiquée. Promo InfoBref: pour l'essentiel des nouvelles en 5 minutes, inscrivez vous à https://infobref.com/utdt Dans cet épisode: YouTube étiquette les vidéos d’IA Intel Arc G3: des puces pour le gaming mobile Cohere et Mila développent une IA plus québécoise Facebook+ au Canada: 4$US par mois pour Facebook et Instagram Ottawa va revoir son projet de loi C-22 sur l’accès légal aux données mobiles 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: Google Fitbit Air: un bracelet santé minimaliste… qui hallucine? Anker Soundcore Liberty 5 Pro Max: des écouteurs épatants et pas cher Et plus! Voir https://www.cogecomedia.com/vie-privee pour notre politique de vie privée

Natürlich geht es heute um die SpaceX-S1-IPO-Filings, aber vorher sprechen wir über die Google I/O (Universal Cart, Gemini Spark, Gemini 3.5 Flash und viele mehr). Wir vergleichen Umsatz und Verlust (Gewinn) von OpenAI und Anthropic. Andrej Karpathy wechselt zu Anthropic, Cursor erreicht $3 Mrd. Annual Sales Rate. Starlink ist die SpaceXs Cashcow ($11 Mrd. Umsatz, $4,4 Mrd. Profit) und subventioniert die mit 12,5% wachsende KI-Sparte. OpenAI kündigt am Tag des S1-Filings überraschend frühen IPO an. Binance launcht SpaceX Pre-IPO Perpetuals. Bezos hat sich diese Woche auch zu Wort gemeldet. Zudem sprechen wir über Forum-AI-Studie: Falsche News-Antworten von KI, Google holt Contextual-AI-Team für $100 Mio, Airbnb erweitert auf Hotels und Mietwagen. Earnings von Nvidia und Workday. SAP, Mistral und unser Digitalminister Wildberger lässt offenbar Texte/Reden von schreiben. Unterstütze unseren Podcast und entdecke die Angebote unserer Werbepartner auf ⁠⁠⁠⁠⁠⁠doppelgaenger.io/werbung⁠⁠⁠⁠⁠⁠. Vielen Dank!  Philipp Glöckler und Philipp Klöckner sprechen heute über: (00:04:00) Google I/O Recap (00:25:44) OpenAI Q1 Earnings: $5,7 Mrd. (00:34:33) Anthropic Q1 & profitable im Juni (00:41:21) Karpathy zu Anthropic (00:42:40) Cursor bei $3 Mrd. Runrate (00:44:39) SpaceX S1 Filing Deep Dive (01:19:30) SpaceX kauft Cybertrucks für $140 Mio. (01:24:30) OpenAI IPO-Filing kommt früher (01:28:29) SpaceX Pre-IPO Perpetuals (01:32:37) Arbeiter stirbt in Starbase (01:33:28) Bezos: Space-Datacenter & Steuer-Debatte (01:38:34) Forum AI: GROK unzuverlässig bei News (01:41:19) Google holt Contextual-AI-Team für $100 Mio. (01:41:51) Airbnb: Hotels, Mietwagen, Everything-Travel (01:43:39) Nvidia Earnings +85% (01:46:51) Workday Earnings +14% (01:47:22) Zuckerberg-Audio: Mitarbeiter-Spionage (01:47:44) Enhanced Games (Steroid-Olympics) (01:52:21) Reuters: GROK 3 von 400 US-Behörden-Fällen (01:53:12) WaPo: DOGE-Datenzugriffe geheim (01:54:04) Trump schützt sich vor IRS (01:54:46) Cohere übernimmt Reliant AI Shownotes Google I/O 2026: Größte AI-Ankündigungen - theverge.com OpenAI behält $1 Mrd. Umsatz-Vorsprung vor Anthropic in Q1 - theinformation.com OpenAI Action-Figur-Werbung auf Instagram - instagram.com Anthropic wird erstmals profitabel - wsj.com Andrej Karpathy wechselt zu Anthropic - bloomberg.com Cursor erreicht $3 Mrd. Annual Sales Rate - bloomberg.com SpaceX-IPO: Founders Fund vor $60 Mrd. Return - theinformation.com OpenAI IPO-Filing kommt früh - wsj.com OpenAI klaut SpaceX die Show mit IPO-Ankündigung - marketwatch.com Binance launcht Pre-IPO Perpetuals für SpaceX - prnewswire.com SpaceX: Arbeiter stirbt in Starbase - futurism.com Bezos / Blue Origin: Data Center im All - cnbc.com WOLF Financial Tweet (bitte manuell prüfen) - xcancel.com Studie: ChatGPT, Claude, Gemini, Grok bei News unzuverlässig - bloomberg.com Google: $100 Mio. Acqui-License von Bezos' Contextual AI - bloomberg.com Airbnb fügt Hotels und Mietwagen hinzu - cnbc.com Nvidia-Earnings: +85% durch AI Boom - theguardian.com Workday Q1 Earnings: Aktie +14% - cnbc.com LayoffAI Tweet - xcancel.com Steroid Olympics - ft.com Christian Angermayer und die Enhanced Games - theguardian.com Grok fällt in Washington durch: Nur 3 von 400 Behörden-Fällen - reuters.com Behörden verweigern Auskunft über DOGE-Datenzugriffe - washingtonpost.com Trump schützt eigene Steuererklärungen vor IRS-Prüfung - spiegel.de Cohere übernimmt deutsches KI-Startup Reliant AI - manager-magazin.de Reliant-AI-Gründer Karl-Moritz Hermann verkündet Cohere-Deal - linkedin.com Schreibt ChatGPT die Reden des Digitalministers? - de.linkedin.com

Hey, Alex here, just got back from the sunny Shoreline Theater in Mountain view, so let me catch you up! This week was definitely Google heavy, we are covering Google's IO conference for the third year in a row, and today we have a special guest, Logan Kilpatrick, is joining to discuss the announced Gemini 3.5 Flash, Google Omni model, and the new Managed Agents offerings. Plus, this week, for the first time, OpenAI announced that AI solved a Math problem that humans couldn't solve for 80 years, Cursor is showing off Composer 2.5 which is partly trained on XAI data, Karpathy joins Anthropic and much more! Let's dive in! P.S - We've announced our upcoming hackathon, Weavehacks-4, June 6-7, I'll be there, we're expecting the seats to run out very soon so register nowThursdAI - We'd love to have your subscription, and if you're already subscribed, please hit that bell on YT to never miss an episode!Google I/O 2026 - Google goes agentic everywhereI went to cover Google I/O for the third year in a row, shoutout to the DeepMind team for inviting ThursdAI again, and folks, this one felt different.Last year, Google I/O was still very model-centric. This year, the story was not “here is another benchmark chart.” The story was: Google is putting Gemini into everything, and the agentic layer is becoming the product layer. Search, Gemini app, Android, Workspace, YouTube, AI Studio, Cloud, Antigravity, Flow, managed agents, smart glasses, all of it is now orbiting around one pretty clear strategy: Gemini is the intelligence, Antigravity is the agent harness, Google's products are the distribution. I saw many reactions that were milquetoast, as in, “we expected more” and those seem to dominate the X feed. But I think the distribution is the part that many folks on X are missing. Yes, we can argue about Gemini 3.5 Flash pricing. Yes, we can argue whether “Flash” still means what Flash used to mean. But when Google says the Gemini app itself has 900 million monthly active users, before even counting Search, Gmail, YouTube, Docs, Drive, Android, and the rest of the Google surface area, that's massive! OpenAI ChatGPT is supposedly stagnated at ~900M, I don't remember them crossing a 1B. Meanwhile Google is gaining traction. And they just updated all those folks with a new model!Wolfram said it really well on the show: his mother is not sitting there reading model cards. She just uses her Pixel, voice unlocks Gemini, asks for help, and suddenly the default intelligence available to her goes up. Antigravity 2.0 - the agent harness takes center stageThe biggest strategic signal from Google I/O for me was Antigravity.Remember, Antigravity was an IDE that came from the Windsurf acquisition saga. Part of the Windsurf team went to Google, part went to Cognition, and now Google is very clearly putting Antigravity in the middle of its agentic future. And I mean very clearly. Sundar mentioned it. Demis mentioned it. Varun Mohan the co-founder was on stage immediately after them! If you've ever watched a Google I/O keynote, you know how carefully every minute is allocated. Google has YouTube, Search, Gmail, Android, Cloud, Ads, Workspace, and a thousand VP-level products that could be on stage. The fact that Antigravity was that prominent should tell you everything.Logan Kilpatrick joined us and framed this in a way I loved: Gemini became the through-line across Google products, and now the Antigravity agent harness is becoming the through-line for agentic experiences.The new Antigravity 2.0 is a complete overhaul, showing only an agentic interface (which was previously just a separate window called Agent Manager) and separating the IDE layer completely into its own app and showing a Codex like agent-first interface, which got a few folks furious. This move may be weird to some folks, but if you follow along where everyone's going, this seems to be the way of the future, coding is no longer about lines of code, it's about managing fleets of agents. The new Gemini 3.5 absolutely shines inside the new Antigravity, the model was trained with this harness in mind, and is currently offered at an incredible speed (12x), so I'm definitely going to try it! Gemini 3.5 Flash - fast, determined, and maybe not the old “Flash”The most debated model release of the week was Gemini 3.5 Flash.Some folks saw the pricing and token usage and immediately went “this is not Flash.” I get that reaction. Flash used to mean cheap, fast, lightweight chat model. But Logan's framing on the show was important: Flash is now being built for the agentic era.In a chat era, you optimize for one user message and one model answer. In an agentic era, the real token volume is in tool loops, intermediate reasoning, retries, file reads, web searches, code execution, and self-correction. That's a different product profile.Wolfram already ran Gemini 3.5 Flash through WolfBench, and the results were fascinating. With the Hermes agent harness, Gemini 3.5 Flash hit an 87% ceiling on Terminal Bench 2.0, meaning across runs it could solve more of the benchmark than even GPT-5.5 extra high in that setup. The variance was higher with the simpler Terminus harness, but with a real agent harness, the model looked much stronger.That tracks with what Nisten saw in his “Martian railgun from Olympus Mons” test. Gemini 3.5 Flash went extremely detailed, almost too determined, kept correcting itself, overcorrecting itself, and built a whole game-like simulation. Logan laughed and basically said: yeah, this model is very determined, possibly an overcorrection from the “Gemini is lazy” feedback. It also tracks with the mismatch in other benchmarks, in some, Gemini 3.5 flash shines (like the above Apex-agents from AA) and in some, it doesn't match the other frontiers. In my tests, it was definitely over-eager to use a million and a half tool calls, read tons of files, to just help me review this draft inside antigravity. It's like a super eager robotic golden retriever! Gemini Omni - Nano Banana for video, but actually more than thatThe biggest update from last year IO was Veo 3! This year, the biggest wow factor was also visual, but it wasn't VEO 4, it was a new model that is multimodal, trained end-to-end they call Omni. Google is calling this their first “create anything from anything” model, and the first version, Gemini Omni Flash, starts with conversational video editing. The easy description is: Nano Banana for video. You upload or create a video, then talk to it. Change this character. Replace this person. Add an object. Make this scene claymation. Keep the scene, but change the environment.I played with it live and showed a few examples. I asked for a claymation explainer of protein folding, then gave it my face and asked it to replace the character with me. It did it. I uploaded pictures of Sonia, my cat, and it generated a talking cat video with the right kind of cat teeth, which is weirdly important because so many pet generations accidentally add human teeth and become nightmare fuel.The failure modes are still there. I asked it to make Sonia a Russian-speaking female cat, and it only partly switched languages and didn't really change the voice. Audio upload support is also not fully productized yet, even though the underlying model is multimodal. But the direction is very clear.This is not just “Veo with a chat model glued on.” I asked Jeff Dean - Google's chief scientist about this at I/O, and he explained that Omni is trained end-to-end. The intelligence and the generative media capabilities are part of the same model family, not a hacky two-model pipeline. He also said the intelligence is around a recent Flash-level model, which is a big deal when you think about video editing as reasoning over physics, identity, scene continuity, and intent.A lot of people compared Omni to Seedance 2.0, and I think that's the wrong comparison. Seedance is amazing at cinematic generation (lkaregly due to lack of copyright concerns from Bytedance). Omni's unlock is iterative editing on real footage and coherent multi-turn creative control. Other Google IO 2026 releases I found notableThis was a concentrated effort of a huge company to insert AI into every product surface they have so of course I can't cover ALL of it here, but the most notable things for me were: * Gemini Spark - a new agentic experience from Google, to help you with tasks across Gmail, Drive and more. It should support skills, and is a de-facto OpenClaw/Hermes alternative from Google for regular folks. It's not “yet” live so we'll talk more about it when I can test it out* Managed Agents in the Gemini API - We chatted with Logan about this one, Google is re-imagining how agents are going to get built, and are offering 1 api call to spin up an agent in a full Linux env, with security and sandboxing in mind. I'll expand more on this in a next episode, as I recorded a complete conversation about this with Ali Çevic, a PM for Google APIs* AI overhaul of Google Search - AI Overviews will not expand into AI mode, and the iconic Google search box itself will change, for the first time in 25 years to include AI mode! * SynthID expantion and OpenAI collab - Google showed off that OpenAI is joining in marking all AI generate imagery and video with an invisible SynthID watermark. I think this is amazing and more companies should adopt this standard* AI Glasses! We got Google Glasses demos - Together with Warby Parker and Gentle Monster, Google finally showed off their answer to Meta Raybans/Oakleys. They look like regular glasses too, but can hear and talk to you, with the full power of Gemini multimodality. Available in the fall sometime! * Demis Hassabis “we're on the cusp of the singularity” closer - CEO and Co-Founder of DeepMind, Demis Hassabis, closed the show with his remarks about the positive future and that we are nearing this Singularity point after which the future is very uncertain. I found it to be very inspiring and closed our show with that clip as well! * Personally, I got to chat to: Demis Hassabis, have breakfast with Jeff Dean, ask Josh Woodward a bunch of questions, and pester about 20 other great folks on a live stream, and had a lot of fun! Huge thanks to the DeepMind folks, Lucie, Dimple, JD and many others for the continued belief in ThursdAI and invite me to cover this great event. OpenAI LLMs solve an 80yo math problem - Erdős Unit Distance ConjectureOutside of Google I/O, the biggest story of the week was OpenAI announcing that a general-purpose reasoning model made progress on the Erdős planar unit distance problem.This problem goes back to 1946. For nearly 80 years, mathematicians believed the best constructions looked roughly like square grids. OpenAI's model found a new family of constructions with a polynomial improvement, using algebraic number theory ideas that humans apparently had not explored in this context. The above is a representation of it! Important caveat: this does not fully solve every version of the asymptotic Erdős conjecture. Some mathematicians are pushing back on the framing, and fair enough. Precision matters. But even with the caveat, this is still a huge moment.The reason it matters is not that I personally understand the math. I absolutely do not. The reason it matters is that this was not a special-purpose IMO model fine-tuned only for math competitions. This was a general-purpose reasoning model exploring a real open problem, generating candidates, verifying them, and finding a path humans hadn't taken. Extrapolate this to other sciences, Physics for example? This means an amazing future. LDJ pointed out that mathematicians have been skeptical because there have been previous false alarms. But this one landed differently. When Fields Medalist-level mathematicians verify the proof, the discourse changes from “lol stochastic parrot” to “wait, what does this mean for my PhD?”My answer is: yes, still study math. Please study math. The mathematicians who use these tools will do much more than people who don't understand the domain. Same with software engineering. Senior engineers with Codex, Claude Code, Hermes, Antigravity, Cursor and other agents are becoming dramatically more effective because they can steer, evaluate, and recover the work.This being published a day after Demis's “foothills of the singularity” is a great conjecture. Cursor Composer 2.5 - Opus 4.7 performance model from Cursor, at 10x better efficiencyCursor dropped Composer 2.5, and folks, this is a serious release.Composer 2.5 is built on Moonshot's Kimi K2.5 base, like Composer 2, but Cursor scaled the post-training dramatically. They used 25x more synthetic tasks and introduced targeted textual feedback during RL rollouts, where the model gets hints inserted at the point of failure instead of only getting a noisy final reward.The benchmark story is strong: around 69.3 on Terminal Bench 2.0, basically neck and neck with Opus 4.7 in Cursor's chart, and strong results on SWE-bench multilingual and CursorBench. The pricing is the part that makes this especially interesting: $0.50 per million input tokens and $2.50 per million output tokens, with a faster variant at $3 / $15. That is much cheaper than the frontier models it is trying to replace for day-to-day coding work.Cursor engineers are reportedly dogfooding Composer 2.5 heavily and rarely switching away. That matters more to me than any single benchmark. If the people building Cursor can use it as a daily driver, that is a very real signal.The wild part is what comes next. Cursor is partnering with SpaceXAI to train a much larger model from scratch using 10x more compute on Colossus 2. Cursor has the workflow data. xAI has enormous compute. If this works, Cursor stops being just the IDE company and becomes a coding-model lab.We've been saying for months that coding agents are the path toward general agents. Anthropic has Claude Code. OpenAI has Codex. Google has Antigravity. xAI has Grok Build. Cursor has Composer. I'm looking forward to seeing how well it performs on our own benchmarks! Anthropic, xAI, Karpathy, and the compute warsThe compute story this week was bonkers.The SpaceX IPO filing reportedly revealed that Anthropic is paying SpaceXAI $1.25B per month for AI compute at the Memphis Colossus facility. Per month. That's about $15B a year, through May 2029, for access to more than 220,000 NVIDIA GPUs including H100s, H200s and GB200s.This is apparently inference compute for Claude Pro, Max and API users, not training. And it explains a lot of the recent quota changes. Anthropic doubled some Claude usage limits, and suddenly the product feels less constrained.Also, can we just acknowledge the comedy here? Elon Musk publicly called Anthropic “misanthropic,”, went off against every competitor to XAI, is now selling spare GPU time to Cursor and Anthropic? Who's next, OpenAI? The bigger point is that the AI capex story is no longer just NVIDIA. It's also whoever owns the data centers, power, cooling, networking, and GPU clusters. Compute is becoming the land under the AI economy.Also, Andrej Karpathy joined Anthropic. Karpathy could work anywhere. He co-founded OpenAI, led Tesla Autopilot vision, taught half the AI world how neural nets work, and now he's going back into frontier LLM R&D at Anthropic.Open source LLMs - Cohere, Qwen, NousOpen source had a strong week too.Cohere released Command A+, a 218B total parameter sparse MoE model with only 25B active parameters per token, under Apache 2.0. This is their first model that unifies reasoning, vision, multilingual, tool use and citations in one package.The hardware story is great: W4A4 quantization can run on 2 H100s or a single B200. Cohere says it supports 48 languages, 128K input context, 64K output, and gets big jumps over Command A Reasoning, including Tau-squared Bench Telecom from 37% to 85% and Terminal-Bench Hard from 3% to 25%.Cohere is one of those labs that doesn't always chase the loudest consumer hype, but they are very serious on enterprise and multilingual. Apache 2.0 makes this one especially useful.Alibaba also dropped Qwen 3.7-Max, positioned as an agentic frontier model. The headline from their testing is wild: 35 hours of continuous autonomous operation with more than 1,000 tool calls. They also showed it controlling a physical robot inside Alibaba offices and finding an umbrella after about 20 minutes of agent interaction.This digital-to-physical bridge is where things start feeling very real. An agent loop that can write code and use tools can also navigate physical tasks if you give it the right robotics stack.And our friends at Nous Research released Lighthouse Attention, a sparse attention method for long-context pretraining. At 512K context, they report a 17x faster forward+backward pass than standard attention on a single B200, and the recovered checkpoints actually beat dense-from-scratch final loss at the same token budget.The clever part is that the selection logic sits outside the attention kernel, so you still use regular FlashAttention on a gathered dense subsequence. No custom sparse kernel nonsense. If this holds up, this could matter a lot for long-context training.Tools and agentic engineering - X subscriptions, Grok Build, Codex MobileOne really practical tool update: Hermes and OpenClaw can now use your X subscription directly.This is more important than it sounds. You can connect your X Premium subscription and get access to semantic X search and Grok-related tooling without using sketchy browser automation or unofficial APIs that might get you banned. Wolfram already used this to have his agent go through his likes and bookmarks from the past week and send me news items for the show. That is exactly the kind of “small but real” agent workflow that becomes addictive.xAI also launched Grok Build, their agentic CLI coding tool, in early beta for SuperGrok Heavy subscribers. Early users are already running parallel Grok Build agents through tmux supervisors and using it for more than coding: fleet data triage, security patching, training label work, and general automation.The pricing being discussed is aggressive, around $1 per million input tokens and $2 per million output tokens for the API. The model version is grok-build-0.1, and folks have already wired it into Hermes with a 256K context window.And then there's Codex Mobile, which OpenAI shipped inside the ChatGPT mobile apps. This is one of those releases that sounds small until you start using it. You can control Codex sessions remotely from your phone, connected to your machine, and because Codex has native connectors to Gmail, Calendar and other surfaces, it sometimes feels faster and more reliable than local CLIs duct-taped to third-party integrations.I ported Wolfred into Codex with skills and everything, and I've been comparing the same tasks in Hermes and Codex. Codex is often faster, not necessarily because the model is always smarter, but because the connectors and harness are cleaner. Harness matters. We keep coming back to this.This Week's Buzz - W&B, CoreWeave, WolfBench and roboticsThis week in the Buzz, Wolfram walked us through a few things from the Weights & Biases / CoreWeave world.CoreWeave is a gold sponsor at ICRA 2026 in Vienna, the International Conference on Robotics and Automation. NVIDIA is also going big there with a keynote on generalist humanoid robots, 17 accepted papers and workshops around sim-to-real, robot foundation models, autonomous driving, manipulation, and physical AI.Wolfram will be there later in the week, after speaking at the AI Developer event in Cologne about WolfBench. If you're in Europe and into robotics or agent evals, find him.We also looked at WolfBench results for Gemini 3.5 Flash, which honestly became one of the more interesting empirical points of the episode. The model looks variable in simple harnesses, but very capable in better agent loops. That's the whole thesis of measuring model + harness together instead of pretending the model card tells the whole story.The water discourse, almonds, and data center realityWe also got into the data center water discourse, because this talking point is everywhere right now.There are real infrastructure questions around AI. Power, land, cooling, grid capacity, permitting, local impact, all of that matters. But the “AI is stealing drinking water” version of the argument is often wildly detached from scale.The stat I brought up on the show: California almonds use roughly 3 to 5.5 million acre-feet of water per year, multiple times more than all North American data centers combined in 2025. Nisten and LDJ added the important cooling nuance: many large data centers use closed-loop cooling, and evaporative cooling is not universal. Some data centers can avoid water use almost entirely, but at the cost of higher electricity usage.This doesn't mean “no concerns are valid.” It means if we're going to regulate or pause data centers, let's be honest about the actual tradeoffs. AI compute is becoming the substrate for medicine, robotics, science, logistics, software, education and every other productivity layer. We should build responsibly, but not based on viral fear math.Closing thoughts - foothills of the singularityDemis closed I/O saying we're in the foothills of the singularity, and I know how that lands when you write it down. But I was in the room, and after the keynote he told me something I haven't been able to shake: he thinks AI is going to be 10x as impactful as the Industrial Revolution, and 10x as fast. Basically 100x. This is the AlphaFold guy. Not someone loose with his words.Then look at the week. A general reasoner cracked an 80-year-old math problem. Cursor is training near-frontier coding models on a fraction of the big-lab budget. Anthropic is paying Elon $15B a year for inference. Karpathy left education to go back into pre-training. Google rolled out an intelligence uplift to a billion people who don't even know a model dropped.If you put that on a whiteboard in 2023, it reads like a sci-fi pitch.LDJ's mathematician friends are asking if they should keep doing their PhDs. My answer hasn't changed: yes, please keep going. The people who combine domain taste with these tools are going to ship more in 5 years than the previous generation did in 50. The tool doesn't replace the taste. It just removes the bottleneck.That's the whole reason ThursdAI exists. Not to hype every drop, not to dunk for engagement, but to give you a shot at being one of the people who knows what's happening, with the receipts.This week, a lot changed.See you next Thursday.TL;DR and Show Notes* Hosts and Guests* Alex Volkov - AI Evangelist at Weights & Biases / CoreWeave, @altryne* Co-hosts: @WolframRvnwlf, @nisten, @ldjconfirmed* Guest: Logan Kilpatrick, MTS at Google DeepMind / AI Studio, @OfficialLoganK* Google I/O 2026* Google went all-in on agents across Search, Gemini, Antigravity, Workspace, Android, Cloud and YouTube (I/O site, Alex thread)* Antigravity 2.0 became the central agentic coding harness across Google (Sundar, Google OS demo)* Gemini 3.5 Flash launched as a fast, determined workhorse model for agentic loops (Logan, Noam Shazeer, Jeff Dean)* Gemini 3.5 Flash is rolling out across the Gemini app, Search AI Mode, Gemini API, Google AI Studio, Antigravity and Gemini Enterprise Agent Platform (Koray Kavukcuoglu)* Google Search is getting new Gemini 3.5 Flash-powered agentic capabilities, including a new AI-powered Search box and background information agents (Sundar)* Gemini Spark was announced as a 24/7 personal AI agent that can proactively work across Google surfaces (News from Google)* Google teased Gemini-powered Android XR smart glasses with eyewear partners Gentle Monster and Warby Parker (Google, Alex live reaction)* Google AI Studio and the Gemini API got major agentic developer updates, including Managed Agents (Google AI Developers)* Vision & Video* Google DeepMind launched Gemini Omni, a “create anything from anything” multimodal model starting with conversational video editing (DeepMind, Google DeepMind on X)* Omni is available in the Gemini app, Google Flow and YouTube, with API support coming soon (Logan, Gemini App, Sundar)* Key distinction: Omni is not just text-to-video, it is an iterative multi-turn video editing model that combines Gemini intelligence, world knowledge, multimodal inputs and generative media (Google)* Big CO LLMs + APIs* OpenAI announced a general-purpose reasoning model made progress on the Erdős planar unit distance problem, challenging an 80-year-old mathematical belief (OpenAI, X)* Cursor launched Composer 2.5, built on Kimi K2.5, with Opus-class coding performance at much lower cost (Cursor blog, X)* Alibaba released Qwen 3.7-Max, an agentic frontier model with long autonomous runs and robotics demos (Qwen blog, X, robot demo)* Andrej Karpathy joined Anthropic to work on frontier LLM R&D (X)* SpaceX IPO filing revealed Anthropic is paying $1.25B/month for AI compute at the Memphis Colossus facility (Axios, Sawyer Merritt)* The jury in Musk v. Altman found Musk's OpenAI claims barred by statute of limitations, with Musk saying he will appeal (Elon Musk, Sawyer Merritt, Max Zeff)* Open Source LLMs* Cohere released Command A+, a 218B MoE model with 25B active parameters under Apache 2.0 (Cohere, Nick Frosst, HF W4A4, HF BF16)* Nous Research released Lighthouse Attention, a sparse attention method for long-context pretraining with major speedups (Blog, X, arXiv, GitHub)* Tools & Agentic Engineering* Google launched Managed Agents in the Gemini API, letting developers spin up hosted Antigravity agents with Linux sandboxes and persistent state (Docs, X)* xAI launched Grok Build, an agentic CLI coding tool in beta for SuperGrok Heavy users (xAI CLI, X)* Hermes and OpenClaw can now use X subscription auth for semantic search and Grok tooling (Alex)* OpenAI Codex Mobile is now available in the ChatGPT mobile apps for remote agent workflows (OpenAI)* Anthropic doubled Claude usage outside peak hours for a limited period, including Claude Code and other Claude surfaces (Claude)* This Week's Buzz - W&B / CoreWeave* Weights & Biases by CoreWeave is at ICRA 2026 in Vienna, with robotics and automation taking center stage (ICRA, W&B event page)* NVIDIA heads to ICRA 2026 with robotics work around generalist humanoids, physical AI and sim-to-real systems (NVIDIA Robotics, NVIDIA ICRA)* Wolfram is speaking about WolfBench at the AI Developer event in Cologne before heading to ICRA in Vienna (Wolfram)* Other Topics* Data center water usage discourse came up again, including why comparisons need real scale and context rather than viral fear math* The broader theme of the week: coding agents are becoming general agents, and the major labs are now competing on the full stack of model, harness, tools, context and compute This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit sub.thursdai.news/subscribe

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.

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.

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

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