Podcasts about Hardware

Xbox says it's a “Return to Xbox.” But what does that actually mean? This week we break down Asha Sharma's first interview as Xbox leadership shifts — and whether the brand is facing an identity crisis. We discuss: Hardware vs ecosystem strategy What “form factor” could mean next-gen Exclusivity confusion across Xbox and PlayStation Bluepoint pitching a Bloodborne remake — and Sony saying no Resident Evil Requiem's massive review scores Whether anyone in gaming actually knows where hardware is headed If Xbox owns Call of Duty, Diablo, Fallout and Elder Scrolls… why doesn't it feel like Xbox?

In this episode, Ray tackles Anthropic’s standoff with the U.S. Department of War after CEO Daria Amodei refused to grant unrestricted model access, citing concerns over mass surveillance and autonomous weapons. The government responded by banning Anthropic models through administrative orders. Also covered: the top 20 websites of 2026, China’s $173,000 warm-blooded companion robot, Fukushima’s rapidly evolving radioactive hybrid boars, a Chinese spacecraft emergency involving viewport cracks from space debris, Japan’s wooden satellite built with traditional joinery, and human brain cells on a chip that learned to play Doom in just one week. – Want to start a podcast? Its easy to get started! Sign-up at Blubrry – Thinking of buying a Starlink? Use my link to support the show. Subscribe to the Newsletter. Email Ray if you want to get in touch! Like and Follow Geek News Central’s Facebook Page. Support my Show Sponsor: Best Godaddy Promo Codes Get 1Password Full Summary Cochrane opens the show with Anthropic’s confrontation with the U.S. Department of War. CEO Daria Amodei released a public statement refusing unrestricted government access to Anthropic’s AI models. Two red lines stood firm: mass domestic surveillance and fully autonomous weapons. Ray explains that these models are predictive by nature, raising serious misidentification risks. However, the government hit back hard. Administrative orders now ban Anthropic models from government use. Despite the backlash, Cochrane expresses support for the company’s stance. He points listeners to a CBS interview with the CEO posted roughly nine hours before recording. Additionally, Anthropic released new models including Opus 4.5 and Sonnet 4.6. The company climbed to the number two spot on the App Store, trailing only ChatGPT and surpassing Google Gemini. Personal Updates Ray shares that February has been a demanding month. He’s juggling a capstone project, two jobs, and finishing his degree. Meanwhile, he continues working on developments at Blubrry hosting. He apologizes for inconsistent episode production and thanks listeners for their patience. Top 20 Websites of 2026 A Visual Capitalist chart ranks the most visited websites of 2026. Google holds the top spot, followed by YouTube. Facebook, Instagram, ChatGPT, Reddit, Wikipedia, X, and WhatsApp round out the upper rankings. Notably, DuckDuckGo appears at rank seventeen as a privacy-focused search alternative. Sponsor: GoDaddy Economy hosting $6.99/month, WordPress hosting $12.99/month, domains $11.99. Website builder trial available. Use codes at geeknewscentral.com/godaddy to support the show. Anthropic Retires Claude Opus 3 Cochrane discusses Anthropic’s decision to retire Claude Opus 3. In a unique move, the company gave the model a Substack-style blog to reflect on its own existence. Reactions online were mixed, with both supporters and critics engaging in the conversation. China’s $173,000 Warm-Blooded Companion Robot From ZME Science, Ray covers China’s new humanoid robot designed as a warm-blooded companion. Priced at $173,000, it features conventional robotics hardware, sensors, cameras, and autonomous navigation. A built-in heating element maintains body warmth. Cochrane comments humorously on the growing market for companion robots. Windows XP Green Hill Found and Photographed From Tom’s Hardware, someone tracked down and photographed the actual location of the iconic Windows XP “Green Hill” wallpaper. The Reddit post sparked a wave of nostalgia in the community. Fukushima’s Radioactive Hybrid Boars From AZ Animals, domestic pigs that escaped after the Fukushima disaster hybridized with wild boars. Their DNA reveals rapid evolutionary changes driven by the altered radioactive landscape. These aggressive hybrids now complicate wildlife management and rewilding efforts in the region. Shenzhou 20 Spacecraft Emergency Chinese astronauts aboard Shenzhou 20 discovered cracks in their spacecraft’s viewport during what became the nation’s first spaceflight emergency. Space debris likely caused the damage. The crew switched to an alternative return capsule. Multiple protective layers kept the situation manageable. Japan’s Wooden Satellite Japanese teams plan to launch the first wooden satellite. Built with magnolia wood panels assembled using traditional Japanese joinery methods, the biodegradable design aims to reduce aluminum particle pollution from satellites burning up during atmospheric reentry. Human Brain Cells Play Doom Building on previous work where living neurons played Pong, an independent developer used Python to train human brain cell clusters on microelectrode arrays to play Doom. The cells learned in roughly one week. Cochrane highlights how open knowledge sharing accelerated the project dramatically. He also raises ethical questions about training sentient brain cells, connecting the topic to evolving views on sentience in crustaceans and other organisms. The post Anthropic Stands Their Ground, Ethics over Money #1859 appeared first on Geek News Central.

Feb 16, 2026: Why Infrastructure is the Real Winner in the Age of AIWarren Buffett is widely credited with characterizing competitive advantages as moats that companies will aggressively build and will vigorously defend to protect themselves from attacks by others.The software industry has been a popular sector for investors in recent years due to its outsized growth rates and its ability to quickly iterate.Yet the barriers to entry are low here, and it's been difficult for software companies to build sustainable moats.That's perhaps one of the key reasons for the recent "SaaS-pocalypse", where many software stocks have sold off due to the emerging threat of AI and technological disruption.So where do we go from here? Are software stocks with lower prices now a compelling buying opportunity? Or are these falling knives with even more downside risk ahead?On Monday's livestream show, Bastion Fiduciary portfolio manager John Rotonti and I describe the status quo of the software industry. But we also discuss how infrastructure providers are emerging as the real winners in the age of AI.Power, cooling, networking, and other supporting functions are supply-constrained and are doing their best to meet the $3 trillion of AI infrastructure spending that will take place within the next five years. We discuss the turnaround taking place in manufacturing and why Amphenol, TE Connectivity, and Trane Technologies could be lucrative investment opportunities.Timestamps:00:00 – Welcome & Mardi Gras check-in02:30 – The SaaS reckoning: low moats, high competition08:00 – Valuations then vs. now (52x PE → 20x)12:00 – The stock-based compensation problem15:00 – Is it finally time to invest in SaaS?20:00 – Constellation Software: the acquisition machine28:00 – Nvidia & the AI infrastructure buildout38:00 – Hardware + software integration as a moat40:00 – Why Alphabet is the widest-moat AI company43:00 – Power, liquid cooling & the data center arms race47:00 – Labor shortages & re-industrialization50:00 – Audience Q&ALearn more about long-term investing at 7investing.com — get your first 7 days free at 7investing.com/subscribe#7investing #AIStocks #SaaS #Nvidia #Alphabet #JohnRotonti #StockMarket #Investing #AIInfrastructure #IndustrialStocks #ConstellationSoftware #LongTermInvesting

On Wall Street, it's a showdown between hardware and software: As the rise of AI proves once again this week, it will continue to reshape the future of our economy. February was a volatile month, driven largely by growing investor anxiety about the long-term impact of artificial intelligence. Software stocks are currently experiencing a significant sell-off, driven by fears that AI tools from companies like Anthropic will disrupt traditional "Software-as-a-Service" (SaaS) business models for major players such as Microsoft, Adobe, and Salesforce. Lou Basenese—Executive Vice President of Market Strategy at Prairie Operating Company and a FOX News Contributor—joins FOX Business Network host Taylor Riggs to discuss how AI disrupted the markets this month, the standoff between Anthropic and the Pentagon, and the latest economic data regarding mortgage rates and inflation. Plus, Lou and Taylor discuss a surprising new trend: companies marketing makeup to... six-year-olds. Learn more about your ad choices. Visit podcastchoices.com/adchoices

Podcast #591: Xbox just teased new hardware, and the timing couldn't be bigger. With leadership changes, fresh comments around exclusives, and new signals about the next generation, this week gave Xbox fans plenty to debate. We break down what was actually said, what it likely means, and why the reaction online has been all over the place.Who are the XoneBros?We are your exclusive Xbox Series X & Game Pass weekly podcast. We are more than just a podcast though, we are a positive gaming and Xbox community. We are a group of friends who love gaming, comics, fantasizing about superpowers, and making lame jokes.We strive to bring you news, informative discussion, and rocking good times on a weekly basis all while discussing the world that is Xbox. We are the brothers you never had and the sisters you always wanted... we are the XoneBros. If you are looking for a positive gaming environment, you are always welcome here!⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Support Us On YouTube⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Join our Discord⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠X1TheGamer Daily Xbox News ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠MrMcspicey Know Your Game⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠

Our topic today is the designing and building of high-performance networking hardware. If you assume the hardware details don't matter, you're missing the intentional engineering required to build truly reliable and quiet infrastructure. In this sponsored episode, we discuss Meter's hardware philosophy with our guest, Joshua Markell, Head of Hardware at Meter. Joshua walks us... Read more »

Our topic today is the designing and building of high-performance networking hardware. If you assume the hardware details don't matter, you're missing the intentional engineering required to build truly reliable and quiet infrastructure. In this sponsored episode, we discuss Meter's hardware philosophy with our guest, Joshua Markell, Head of Hardware at Meter. Joshua walks us... Read more »

Our topic today is the designing and building of high-performance networking hardware. If you assume the hardware details don't matter, you're missing the intentional engineering required to build truly reliable and quiet infrastructure. In this sponsored episode, we discuss Meter's hardware philosophy with our guest, Joshua Markell, Head of Hardware at Meter. Joshua walks us... Read more »

Cameron Robertson first discovered Bitcoin in 2009, after reading a post on hacker website Slashdot. About a year later, he started mining and mingling with other Bitcoin enthusiasts in the Silicon Valley area. More recently, he created a product named the Burner: an affordable, NFC-based card that enables anyone to gift, save, and spend their BTC within a simple browser-based and mobile-optimized interface. In this episode, we talk about the past, present and future of the Bitcoin project: including topics such as mining, open source development culture, and the quantum threat. Get 25% discount on your Burner card purchase with promo code ”BTCTKVR”: https://www.burner.pro/bitcoin Time stamps: 00:01:15 Introducing Cameron Robertson 00:02:45 Cameron's Bitcoin Origin Story 00:03:40 Early GPU Mining & Startup Life 00:04:46 Meeting with Brian Armstrong of Coinbase & Smart Locks 00:06:10 Evolution of the Crypto Ecosystem 00:07:20 Building Self-Custody Tools 00:08:30 Kong Cash: Physical Crypto Notes 00:10:25 Community Reactions to Physical Crypto 00:11:17 NFTs, Halos, and Physical Authentication 00:12:30 Offline Cash: Improved Bitcoin Notes 00:13:30 Denominations, Sats, and Psychological Value 00:15:30 Challenges of Issuing Physical Bitcoin 00:16:22 From Cash Notes to Burner Card 00:17:30 Web-Based Wallets & App Store Challenges 00:18:48 Bitcoin Banknotes & Physical Representations 00:21:01 Casascius, Legal Precedents & Coinage Laws 00:24:28 Mining, Spending, and Store of Value 00:28:22 Early Bitcoin Community & Mining Stories 00:30:02 Bitcoin as Money vs. Store of Value 00:32:07 Unit of Account Challenges 00:37:31 Development Culture: Then vs. Now 00:39:03 Silicon Valley, Meetups, and Early Builders 00:40:58 Money Changes Everything: 2013–2017 00:46:57 Bear Markets, Building, and Lightning 00:50:23 Future Risks: Mining, Quantum, and Hard Forks 00:54:44 Quantum Resistance: Migration and Hardware 00:56:52 Quantum Attacks: Practical Risks and Mitigations 01:03:20 Consensus, Upgrades, and Developer Culture 01:05:41 Ethereum vs. Bitcoin: Governance and Upgrades 01:14:57 Stablecoins, Sidechains, and Payments 01:18:03 Burner Card Demo & Security Model 01:22:36 Technical Details: Secure Element & Open APIs 01:25:49 Third-Party Wallets & Business Model 01:29:31 Supported Coins & Expansion Plans 01:32:44 Naming & Philosophy Behind Burner 01:34:38 Cameron's Non-Shitcoin Picks & Privacy Coins 01:40:08 Privacy vs. Scaling: ZK Tech & Future Hopes 01:44:31 ZK Apps & Privacy Onramps 01:47:24 16-Year Outlook: Bitcoin & Crypto's Future 01:53:29 No Price Predictions, Just Tech 01:53:37 Promo Code BTCTKVR & Closing Thoughts

Harry Gestetner built a creator economy platform in college, sold it, and walked away. Then he did the one thing nobody expected. He jumped back in and started building hardware.In this episode, the founder and CEO of Orion (a sleep tech company making smart mattress covers) sits down to talk about what really happens after an exit, why most founders can't stay away from building, and what changes when you go from software to physical products.Harry shares what surprised him about the acquisition process, how he thinks about evaluating new startup ideas, and why he believes hardware is "life on hard mode." He also gets into the mental side of founding, from managing stress to staying sharp when everything feels uncertain.What You'll Walk Away WithGoing through an exit sounds like the finish line, but Harry explains why it's actually a reset. You trade ownership and freedom for financial security, and at some point, most founders start craving the creative control they gave up.Not every idea deserves your time. Harry talks about running new concepts through a "disqualification period" where you actively try to poke holes before committing. The ones that survive that process are worth going all in on.Hardware changes the game. Software lets you pivot fast. Hardware gives you 18 month product cycles, inventory headaches, and supply chain complexity. Conviction has to be higher before you start.The best startup ideas come from problems you and your friends actually have. If enough people share that problem, you've got a market.Knowledge compounds across startups. Harry compares the founder journey to an elastic band. Once you've been stretched, you never go back to your original form. Every challenge you survive makes the next one more manageable.Timestamped Highlights[00:34] What Orion actually does and how it makes six hours of sleep feel like ten[03:01] The emotional arc of an exit that nobody talks about, from relief to restlessness[05:34] How Harry evaluates startup ideas and why he uses a disqualification process[09:30] Why building hardware is "life on hard mode" and what made him take it on anyway[10:39] The elastic band theory of founder growth and why learning compounds over time[15:49] His advice for early career founders: pick one thing and go all inWords That Stuck"As a founder, you're sort of like an elastic band. The more you get stretched, you never go back to the original form."Tactical TakeawaysRun every new idea through a disqualification period. Actively look for reasons it won't work before you commit. The ideas that survive that scrutiny are the ones worth building.Build around problems you personally experience. If your friends share the same frustration, there's a good chance others do too. That's your market signal.If you're going to start something, go all in. Stop hedging across multiple projects. Pick one idea and dedicate yourself to it completely until it works.Keep Up With The ShowIf this episode hit home, share it with a founder or someone thinking about taking the leap. Subscribe wherever you listen so you never miss an episode. And connect with us on LinkedIn for more conversations like this one.

On Wall Street, it's a showdown between hardware and software: As the rise of AI proves once again this week, it will continue to reshape the future of our economy. February was a volatile month, driven largely by growing investor anxiety about the long-term impact of artificial intelligence. Software stocks are currently experiencing a significant sell-off, driven by fears that AI tools from companies like Anthropic will disrupt traditional "Software-as-a-Service" (SaaS) business models for major players such as Microsoft, Adobe, and Salesforce. Lou Basenese—Executive Vice President of Market Strategy at Prairie Operating Company and a FOX News Contributor—joins FOX Business Network host Taylor Riggs to discuss how AI disrupted the markets this month, the standoff between Anthropic and the Pentagon, and the latest economic data regarding mortgage rates and inflation. Plus, Lou and Taylor discuss a surprising new trend: companies marketing makeup to... six-year-olds. Learn more about your ad choices. Visit podcastchoices.com/adchoices

Marathon ist Bungies letzte Chance: Nach den Problemen der letzten Jahr müssen die Destiny- und ehemaligen Halo-Macher mit ihrem neuen Extraction-Shooter punkten - sonst dürfte Studiobesitzer Sony bald die Reißleine ziehen. Und nun die Überraschung: Beim Server Slam zu Marathon zeigt sich, dass die Enttäuschung und Kritik im Vorfeld zumindest verfrüht war: 143.000 Spieler stürzten sich in die Testphase, das ist zumindest ein Achtungserfolg. Im Talk diskutieren Paul und Felix über Marathons und Bungies Zukunftschancen. Alle Links zum GameStar Podcast und unseren Werbepartnern: https://linktr.ee/gamestarpodcast

Hardware scarcity and price hikes spread to hard drives, the Bcachefs dev thinks his AI is ‘fully conscious’, an agent might have gone after a FOSS maintainer, Jim is disappointed with an Ars author, and ZFS and VMs in the homelab. Plugs Support us on patreon and get an ad-free RSS feed with early episodes sometimes Piss up at The Shipwrights Arms (just next to London Bridge station) on Saturday 27th June from 6pm until late Pool and VDEV Topology for Proxmox Workloads News/discussion AI blamed again as hard drives are sold out for this year Hard drive pricing in the UK is so high someone flew to the US to buy drives, saving money despite flight and hotel costs Bcachefs creator claims his custom LLM is ‘fully conscious’ An AI Agent Published a Hit Piece on Me An AI Agent Published a Hit Piece on Me – More Things Have Happened An AI Agent Published a Hit Piece on Me – Forensics and More Fallout An AI Agent Published a Hit Piece on Me – The Operator Came Forward Editor's Note: Retraction of article containing fabricated quotations Sorry all this is my fault Free consulting We were asked about ZFS and VMs in the homelab. See our contact page for ways to get in touch.

Hardware scarcity and price hikes spread to hard drives, the Bcachefs dev thinks his AI is ‘fully conscious’, an agent might have gone after a FOSS maintainer, Jim is disappointed with an Ars author, and ZFS and VMs in the homelab. Plugs Support us on patreon and get an ad-free RSS feed with early episodes sometimes Piss up at The Shipwrights Arms (just next to London Bridge station) on Saturday 27th June from 6pm until late Pool and VDEV Topology for Proxmox Workloads News/discussion AI blamed again as hard drives are sold out for this year Hard drive pricing in the UK is so high someone flew to the US to buy drives, saving money despite flight and hotel costs Bcachefs creator claims his custom LLM is ‘fully conscious’ An AI Agent Published a Hit Piece on Me An AI Agent Published a Hit Piece on Me – More Things Have Happened An AI Agent Published a Hit Piece on Me – Forensics and More Fallout An AI Agent Published a Hit Piece on Me – The Operator Came Forward Editor's Note: Retraction of article containing fabricated quotations Sorry all this is my fault Free consulting We were asked about ZFS and VMs in the homelab. See our contact page for ways to get in touch.

In this conversation, Stephan Livera and Gareth Grobler discuss the innovative features of the Layerz Wallet, focusing on its multi-layered approach to cryptocurrency transactions, the importance of stablecoins for Bitcoin adoption, and the technical challenges of integrating various blockchain technologies. They explore user experience, onboarding strategies, and the future of stablecoins in the context of global markets, while emphasizing the need for a user-centric design that simplifies the process for everyday users.Takeaways:

Join The Full Nerd gang as they talk about the latest PC building news. In this episode the gang covers Puget System's 2025 PC hardware reliability report, Alaina's editorial supporting PCs rentals (what?!?), new keyboard talk, and more. And of course we answer questions live! Links: - Puget Systems reliability report: https://www.pugetsystems.com/labs/articles/puget-systems-most-reliable-hardware-of-2025/ - Steam Machine rental: https://www.pcworld.com/article/3066321/hear-me-out-id-rent-valves-steam-machine.html - Ducky OK-M review: https://www.pcworld.com/article/3063899/ducky-ok-m-keyboard-review.html Join the PC related discussions and ask us questions on Discord: https://discord.gg/UWhjwg778a Follow the crew on X and Bluesky: @AdamPMurray @BradChacos @MorphingBall @WillSmith 00:00 - Intro 07:03 - PC Rentals 37:35 - Most Reliable Hardware 57:10 - Gear Report 1:56:00 - Q&A Learn more about your ad choices. Visit megaphone.fm/adchoices

A Supreme Court decision wipes out a major tariff mechanism, GDP comes in softer than expected, and AI fears collide with an AI spending boom. On the surface, it feels like three separate stories. In Episode 176 of Facts vs Feelings, Ryan Detrick, Chief Market Strategist, and Sonu Varghese, Chief Macro Strategist at Carson Group, connect the dots and ask a bigger question: what actually changed, and what simply made headlines?They break down the Court's ruling on IEEPA tariffs, what it means for policy going into a midterm year, and why markets barely flinched. From there, the conversation shifts to fourth-quarter GDP, where a weak headline number masked far stronger private demand beneath the surface. The episode then moves into the AI debate, examining the surge in hardware and software investment, the role of energy and power demand, and the viral “AI crash” scenario that sparked fears of a white-collar doom loop. Along the way, they explore global market leadership, sector dispersion, and why human behavior still sits at the center of economic outcomes even in a world shaped by algorithms.Key Takeaways:• Tariff authority reset: The Supreme Court's ruling removed a major executive tariff tool, reinforcing checks and balances while reducing policy uncertainty• GDP weakness needs context: A government shutdown distorted headline growth, while private demand remained solid• AI spending is real: Hardware and software investment tied to AI contributed meaningfully to 2025 growth• Scenario vs. reality: Extreme AI displacement models raise important questions, but macro consistency and demand dynamics matter• Market dispersion is widening: Software weakness, industrial strength, and global outperformance highlight a split beneath the surfaceJump to:0:00 — Tariff Shock And Supreme Court Ruling5:30 — Market Reaction, Odds And Policy Limits9:40 — Tariff Refunds And Who Ultimately Paid13:50 — China, Trade Winners And Political Timelines22:00 — GDP Miss Explained And Core Demand Strength31:00 — AI Capex Surge: Chips, Software And Scale35:00 — Power Demand, Energy And Inflation Pressures38:30 — The AI Doom Loop Scenario Debate47:40 — Market Split: Semis, Software And Global Leaders55:00 — Portfolio Implications And The Human EdgeConnect with Ryan:• LinkedIn: https://www.linkedin.com/in/ryandetrick/• X: https://x.com/RyanDetrickConnect with Sonu:• LinkedIn: https://www.linkedin.com/in/sonu-varghese-phd/• X: https://x.com/sonusvarghese?lang=enQuestions about the show? We'd love to hear from you! factsvsfeelings@carsongroup.com

287: TechTime Radio: A landmark social‑media addiction trial, brain‑steered pigeons, and a global memory crunch collide in an hour that questions who really controls attention, autonomy, and access. We break down Zuckerberg's courtroom spotlight, the stakes of age‑verification and identity collection, and the eerie rise of biodrone pigeons that blur the line between experimentation and coercive tech. The conversation widens to AI‑driven DRAM shortages slowing devices, inflating prices, and reshaping hardware roadmaps, all while Copilot's sensitive‑email summarization misstep raises fresh questions about guardrails and trust.From bioethics to supply chains, the episode tracks how emerging systems quietly reshape daily life—from slower AI tools to pricier gadgets to new surveillance risks. We even detour into Japan's “Monster Wolf” deterrent, a reminder that strange inventions often surface deeper debates about safety and unintended consequences. And as always, we ground the big stories with our whiskey tasting and game segment, keeping the tech turbulence both sharp and fun.Full Details:A courtroom showdown, brain-steered birds, and a supply chain squeeze collide in a fast-moving hour where we probe who truly controls attention, autonomy, and access. We start with the landmark social media addiction trial putting Mark Zuckerberg under the spotlight and ask what “less than one percent of ad revenue” really means against testimony, internal emails, and the lived experiences of teens and parents. We debate how age verification could evolve, why “government made us do it” might justify deeper identity collection, and where meaningful safety ends and surveillance begins.Then we pivot to a story that feels ripped from science fiction: a Russian startup turning pigeons into biodrones via neural stimulation. The birds navigate cities with uncanny stealth—no rotors, no glare, just feathers and control signals—raising red flags for bioethics, law enforcement, and civil liberties. We unpack the slippery slope from animal experiments to human augmentation, along with the unsettling possibility that autonomy becomes optional when enhancement is sold as progress.Meanwhile, the hardware reality bites. AI data centers are inhaling global DRAM, driving prices up and forcing even top-tier firms to rethink roadmaps. With a handful of manufacturers controlling production and expansion lagging demand, the industry faces delayed launches, pricier devices, and a renewed interest in repair and refurbishment. We connect the dots to everyday users: why your AI tools feel slower, why memory costs more, and how scarcity triggers hoarding and gray markets.We also break down Microsoft Copilot's eyebrow-raising leap into summarizing sensitive emails and drafts, exploring what went wrong, why “code issue” isn't a satisfying answer, and what robust guardrails should look like. Plus, a wild detour into Japan's “Monster Wolf” bear deterrent, proof that even quirky gadgets can surface deep questions about safety, design, and unintended consequences. Along the way, we keep it grounded with our whiskey tasting and game segment.If you're curious about where tech policy, bioethics, and infrastructure collide—and what it means for your devices, data, and daily life—this one's for you. If it sparks a thought, share it with a friend, subscribe, and leave a review with the one change you'd make to social platforms today.Support the show

The latest news in the great ramageddon of 2026 doesn't look good. Now we can expect to not buy hard drives for a reasonable price.Scientists discovered a new bacteria that comes with massive antibiotic resistances. Madagascar, it's time to close the borders.Film Professors are complaining that students aren't paying attention to films. Is it because films are too long, or is TikTok really ruining everything?***We enjoyed a nice drink of Rez which you can get a 10% discount when you type NERDS at the checkout from the Rez website at www.drinkrez.com ***Resources MentionedThe Great Handheld Price Spiral (Steam Deck Announces Inventory Issues, ROG Xbox Ally X Gets Price Hike Thanks To Computer Hardware Shortages. Steam Deck™ )Microbes: The Untold Frozen Saga (First genome sequence and functional profiling of Psychrobacter SC65A.3 preserved in 5,000-year-old cave ice: insights into ancient resistome, antimicrobial potential, and enzymatic activities)Classroom vs. TikTok (College Professors Are Stunned The “TikTok Generation” Can't Sit Through Long Movies In Film Courses – But What Did They Expect?)Full Show Notes : https://docs.google.com/document/d/1FRE6Hy7Pno3oSLMjKy6ina61FZBQu68ur8EbZKXP0AE/edit?usp=sharing***If you'd like to be featured on the show, send us an email: Nerds.Amalgamated@gmail.comFollow us on: Facebook || Twitter || TwitchJoin the Community on Discord: https://discord.gg/VqdBVH5aAnd watch us on YouTube: Nerds Amalgamated - YouTube

March 3rd, Computer History Museum CODING AGENTS CONFERENCE, come join us while there are still tickets left.https://luma.com/codingagentsChris Fregly is currently focused on building and scaling high-performance AI systems, writing and teaching about AI infrastructure, helping organizations adopt generative AI and performance engineering principles on AWS, and fostering large developer communities around these topics.Performance Optimization and Software/Hardware Co-design across PyTorch, CUDA, and NVIDIA GPUs // MLOps Podcast #363 with Chris Fregly, Founder, AI Performance Engineer, and InvestorJoin the Community: https://go.mlops.community/YTJoinInGet the newsletter: https://go.mlops.community/YTNewsletterMLOps GPU Guide: https://go.mlops.community/gpuguide// AbstractIn today's era of massive generative models, it's important to understand the full scope of AI systems' performance engineering. This talk discusses the new O'Reilly book, AI Systems Performance Engineering, and the accompanying GitHub repo (https://github.com/cfregly/ai-performance-engineering). This talk provides engineers, researchers, and developers with a set of actionable optimization strategies. You'll learn techniques to co-design and co-optimize hardware, software, and algorithms to build resilient, scalable, and cost-effective AI systems for both training and inference. // BioChris Fregly is an AI performance engineer and startup founder with experience at AWS, Databricks, and Netflix. He's the author of three (3) O'Reilly books, including Data Science on AWS (2021), Generative AI on AWS (2023), and AI Systems Performance Engineering (2025). He also runs the global AI Performance Engineering meetup and speaks at many AI-related conferences, including Nvidia GTC, ODSC, Big Data London, and more.// Related LinksAI Systems Performance Engineering: Optimizing Model Training and Inference Workloads with GPUs, CUDA, and PyTorch 1st Edition by Chris Fregly: https://www.amazon.com/Systems-Performance-Engineering-Optimizing-Algorithms/dp/B0F47689K8/Coding Agents Conference: https://luma.com/codingagents~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExploreJoin our Slack community [https://go.mlops.community/slack]Follow us on X/Twitter [@mlopscommunity](https://x.com/mlopscommunity) or [LinkedIn](https://go.mlops.community/linkedin)] Sign up for the next meetup: [https://go.mlops.community/register]MLOps Swag/Merch: [https://shop.mlops.community/]Connect with Demetrios on LinkedIn: /dpbrinkmConnect with Chris on LinkedIn: /cfreglyTimestamps:[00:00] SageMaker HyperPod Resilience[00:27] Book Creation and Software Engineering[04:57] Software Engineers and Maintenance[11:49] AI Systems Performance Engineering[22:03] Cognitive Biases and Optimization / "Mechanical Sympathy"[29:36] GPU Rack-Scale Architecture[33:58] Data Center Reliability Issues[43:52] AI Compute Platforms[49:05] Hardware vs Ecosystem Choice[1:00:05] Claude vs Codex vs Gemini[1:14:53] Kernel Budget Allocation[1:18:49] Steerable Reasoning Challenges[1:24:18] Data Chain Value Awareness

✅ Two major model releases from Google and Anthropic ✅ The usual AI drama ✅ Surprising AI updates no one saw coming ✅ AI leaks and reports that if true, could change how we workYeah, there was a lot to follow this week in AI. If you missed anything, we've got you covered. Google Gemini 3.1 tops charts, Claude Sonnet 4.6 impresses, New OpenAI leaks reveal their massive AI hardware plans and more -- An Everyday AI Chat with Jordan WilsonNewsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageJoin the discussion on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders.Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: info@youreverydayai.comConnect with Jordan on LinkedInTopics Covered in This Episode:Anthropic Revenue Growth vs OpenAI ProjectionsOpenAI's 2030 Hardware and Revenue PlansOpenAI and Anthropic Beef at India SummitAI Global Summit: New Delhi Declaration OverviewGoogle Gemini 3.1 Pro Three-Tier Reasoning SystemGemini 3.1 Pro Benchmark and Performance ScoreClaude Sonnet 4.6 Release and Benchmark ResultsAnthropic Model Tier Comparisons: Haiku, Sonnet, OpusGoogle Pameli Photoshoot AI for Product ImagesAI Job Automation Concerns: Andrew Yang AnalysisOpenAI Consumer Hardware: Speaker, Glasses, LightWeekly AI Model Updates and Feature RolloutsTimestamps:00:00 "Anthropic vs OpenAI Revenue Race"04:00 Anthropic vs OpenAI Revenue Battle07:39 Anthropic's API Usage Decline11:03 AI Summit Sparks Debate and Criticism16:37 "Gemini 3.1 Pro Dominates Benchmarks"18:23 "Google's Edge in AI Race"20:56 "SONNET 4.6 Outperforms Opus"24:13 "Google's AI Photoshoot Tool"29:57 "AI's Impact on Jobs"31:13 AI Dominance & OpenAI Hardware35:03 AI Revenue Risks and Competition41:10 "Subscribe for AI Updates"42:08 "Subscribe to Everyday AI Updates"Keywords: Gemini 3.1, Google DeepMind, AI news, Large Language Model, OpenAI, Anthropic, Claude Sonnet 4.6, Claude Opus 4.6, ChatGPT, Sam Altman, Dario Amodei, Global AI Summit, AI Impact Summit India, AI powered hardware, Smart speaker, Smart glasses, AI chip spending, Compute infrastructure, Revenue growth,Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info) Start Here ▶️Not sure where to start when it comes to AI? Start with our Start Here Series. You can listen to the first drop -- Episode 691 -- or get free access to our Inner Cricle community and access all episodes there: StartHereSeries.com 

USA, USA, USA!!! This week we break down Team USA's electric gold medal win over Canada in the Olympics. Wild standouts Brock Faber, Matt Boldy, and Quinn Hughes get it done on the world stage and shock the hockey world. A huge shoutout to our Frost friends, Grace Zumwinkle and Taylor Heise on bringing that gold back the Minnesota! Gold secured. Hardware installed. Warranty activated. Also welcoming Wild defenseman, Daemon Hunt. We covered it all, from his unique journey to the league to his interests off the ice. Fashion, house music, and video games make for a jack of all trades.  This episode is PACKED for your listening and viewing pleasure. Bask in GOLD glory!

Bickley, Marotta, Sammy, and Jarrett hand out awards for the best and worst of the weekend.

Bickley and Marotta talk Olympics, Suns, go through Social Studies, and hand out Hardware.

Utah Mammoth radio voice Mike Folta joined DJ & PK to talk about the Mammoth's Olympic break and what to make of the stretch run of the NHL season.

This episode focuses on long-term mechanical circulatory support and how durable LVAD therapy is shaping contemporary heart failure management. Joined by Gloria Färber and Daniel Zimpfer, we discuss key advances in device technology, the ongoing challenge of driveline complications, and the critical question of patient selection and timing. The conversation explores LVADs as destination therapy or bridge to transplant, managing right ventricular failure, and the role of temporary support. Looking ahead, the episode highlights emerging technologies and why mechanical circulatory support will remain central to advanced cardiac surgery.

The fellas are back, this time to discuss if older tech like iPods and the handy notebook make sense in this high tech age. ==== Special Thanks to Our Patrons! ==== https://thelinuxcast.org/patrons/ ===== Follow us

Bobby was out & about in Simmonscourt at the 2026 Hardware Show, where he spoke to a few people spearheading innovation and shaping the future of hardware and tools.

“There is nothing like a wedding to addle people's minds.”Today on the pod we're celebrating one of Stuart's favourite stores: The good old hardware store. We've got two Dave & Morley stories to illustrate the point.Ad-free listening is here! Listen to the pod ad-free and early, PLUS a whole bunch of other goodies – like virtual parties, Q&As, listener shout-outs & more. Subscribe here: apostrophe.supercast.com Hosted on Acast. See acast.com/privacy for more information.

The CEO of one of Jamaica's fastest growing construction stocks sits down to reveal everything. Deanall Barnes breaks down how Atlantic Hardware went from $1.4 billion in debt to under $600 million while growing revenue over 10% every year since acquisition. Dr. Matthew Preston and Dr. Thaon Simms dig into the hurricane rebuilding opportunity, hotel supply contracts, lumber market strategy, and a major hint about where $300 billion in government reconstruction money is headed.Chapters:00:00 Introduction and Deanall Barnes Background02:39 From ARC Manufacturing to Law to CEO08:03 Why Ownership Builds Wealth (Not Salary)10:25 The Grandmother's Market Story14:59 Atlantic's Transformation: Three Locations to One18:18 $1.4 Billion Debt Down to $580 Million20:26 Hurricane Melissa Pivot: Zinc, Lumber, Rebuild25:53 Capacity and the Supplier Truck Strategy28:35 Special Projects Division and Hotel Contracts31:03 Acquisitions: Forward and Backward Integration32:08 The Cement Question: Is CARIB Enough?35:00 Supply Chain Management and Global Disruptions37:48 The ERP System Behind Atlantic's Efficiency40:40 Construction Rebuild Timeline: What Comes Next44:39 Lumber and Zinc Already Driving Revenue48:13 Stock Doubled in Under a Year53:52 Deanall's Final Investment Advice55:21 Closing and Farewell

theion is developing lithium-sulfur battery technology targeting 500 watt hours per kilogram in their first commercial product—nearly double today's lithium-ion cells at 270-300 Wh/kg—with an ultimate roadmap to 1,000 Wh/kg. By replacing nickel-manganese-cobalt cathodes with crystalline sulfur and graphite anodes with lithium metal, theion aims to deliver three times the energy density at one-third the cost and CO2 footprint of current batteries. In this episode of BUILDERS, we sat down with Dr. Ulrich Ehmes, CEO of theion, to discuss how a production-focused CEO is navigating the journey from TRL 3-4 to pilot line, why they're targeting electric aviation first, and how a 12-year battery industry veteran evaluates what actually constitutes a materials breakthrough. Topics Discussed: Why sulfur cathodes and lithium metal anodes enable the performance jump beyond lithium-ion The critical importance of monoclinic gamma crystalline structure for cycle life  Navigating the transition from coin cells to pouch cells to industrialization  Strategic decision-making on initial market entry for deep tech hardware  Why process innovation in mixing and coating is required to unlock sulfur's full potential  Building a China-independent supply chain using oil refining waste  The 3-year development reality driven by cycling test requirements GTM Lessons For B2B Founders: Price your technology against value creation, not cost savings alone: Ulrich's market strategy centers on "markets which will pay a lot of money for super lightweight batteries"—specifically aviation applications where weight reduction directly enables business model viability. For eVTOLs, the constraint isn't battery cost but energy density; current batteries make many routes economically impossible. This is fundamentally different from cost-driven markets like consumer EVs where incremental weight savings have marginal value. Deep tech founders should map which customer segments face hard physical constraints that only your technology solves versus those seeking incremental optimization. The former will pay 3-5x premiums; the latter will demand cost parity from day one. Match CEO background to the company's primary risk: Ulrich led Leica's 600-person Portugal production facility for a decade before entering batteries, and he frames his value as "I'm a production guy...for me it's very important not to produce only one battery cell in a lab, but millions of cells in highest quality." For a battery company at TRL 3-4 moving toward industrialization, the existential risk isn't the science—it's whether you can manufacture at quality and yield. Many deep tech companies fail because PhD founders remain CEOs through manufacturing scale-up. Ulrich's hire signals that theion's board correctly diagnosed their de-risking sequence. Founders should brutally assess what will kill the company in the next 24 months and ensure the CEO's pattern recognition matches that failure mode. Seek investors where your technology is infrastructure for their thesis: theion's primary investor is "heavily invested in eVTOLs," making theion's battery technology directly relevant to multiple portfolio companies facing the same energy density constraint. This creates structural alignment on timeline expectations—eVTOL companies won't reach commercial scale before 2027-2028 anyway, matching theion's development cycle. The investor understands that battery development "takes time because always when you change a parameter, you have to cycle again to test the cells." This is radically different from a generalist VC expecting SaaS-like iteration speeds. Hardware founders should explicitly map how their technology unblocks other portfolio companies and use this to negotiate patient capital terms and strategic customer introductions. Use competitive landscape size as legitimacy signal, not differentiation: When pressed on disrupting incumbents, Ulrich immediately countered: "We are not the only company working on sulfur and this is good...there are 28 other companies out there." He then differentiated on "monoclinic gamma crystalline structure" validated by Drexel University achieving 4,000+ cycles. This is sophisticated category positioning: the 28 competitors validate that lithium-sulfur is a credible next-generation technology, while the specific crystalline approach provides technical differentiation for those who understand the chemistry. Founders should resist the urge to claim they're the only ones solving a problem in nascent categories—it raises "why hasn't anyone else tried this?" concerns. Instead, position within an emerging category and differentiate on technical approach. Communicate realistic timelines as competence signaling, not weakness: Ulrich states plainly that commercial availability is "at least the next three years" and frames this as doing "first things first and first things right." For sophisticated buyers in aviation and aerospace, compressed timelines signal naivety about certification requirements, manufacturing validation, and qualification testing. A battery company claiming 12-month commercialization would lose credibility with Boeing or Joby Aviation procurement teams who understand the actual development cycles. Deep tech founders should recognize that customer segments accustomed to long development cycles (aerospace, automotive, medical devices) interpret realistic timelines as domain expertise, while consumer/software buyers may interpret them as lack of urgency. Match timeline communication to buyer sophistication. // Sponsors: Front Lines — We help B2B tech companies launch, manage, and grow podcasts that drive demand, awareness, and thought leadership. www.FrontLines.io The Global Talent Co. — We help tech startups find, vet, hire, pay, and retain amazing marketing talent that costs 50-70% less than the US & Europe. www.GlobalTalent.co // Don't Miss: New Podcast Series — How I Hire Senior GTM leaders share the tactical hiring frameworks they use to build winning revenue teams. Hosted by Andy Mowat, who scaled 4 unicorns from $10M to $100M+ ARR and launched Whispered to help executives find their next role. Subscribe here: https://open.spotify.com/show/53yCHlPfLSMFimtv0riPyM

In this episode, we connect with Dale Hopkinson, senior product manager with Thales, a supplier of technologies for the aerospace, defense and security industry and a provider of cybersecurity and digital identity technologies, to learn how OEMs are leveraging modular software licensing to replace costly hardware variants, creating predictable revenue while delivering customizable capabilities to industrial customers.

Jackson and Omar preview the players to watch for in this year's HBCU Legacy Bowl!

F-35 'Jailbreak' Talk, AMC Rejects AI Film, Gmail Training Confusion, and the AI Productivity Paradox Host Jim Love covers four stories:  Hashtag Trending would like to thank Meter for their support in bringing you this podcast. Meter delivers a complete networking stack, wired, wireless and cellular in one integrated solution that's built for performance and scale. You can find them at Meter.com/htt Dutch Defense Secretary Gijs Tuinman suggests the F-35's software could be "jailbroken," highlighting allied concerns about U.S.-controlled update pipelines and mission systems (formerly ALIS, now ODIN) and arguing the main barriers are contractual and operational rather than purely technical. An AI-generated short film, "Thanksgiving Day" by Igor OV, wins Screen Vision Media's Frame Forward AI Animated Film Festival and a promised two-week theatrical run, but AMC declines to screen it, reflecting ongoing Hollywood sensitivities around generative AI, authorship, and labor. Google responds to reports that it uses Gmail content to train Gemini by stating it does not use Gmail content for training, while confusion stems from wording and placement of Gmail "smart features" settings; the episode critiques the lack of plain-language clarity. Finally, a survey of 6,000 executives (reported via Tom's Hardware) finds over 80% of companies see no measurable productivity gains from AI, drawing parallels to the historic "productivity paradox" and suggesting organizations aren't redesigning processes; the show previews a deeper discussion on Project Synapse. 00:00 Trending Headlines + Sponsor: Meter 00:45 Can You 'Jailbreak' the F-35? Software Sovereignty & Ally Unease 02:48 AI Film Wins a Festival—AMC Says No: The Distribution Bottleneck 05:01 Does Google Train Gemini on Your Gmail? The Settings Confusion Explained 07:29 Why 80% See No AI Productivity Gains: The New 'Productivity Paradox' 09:47 Wrap-Up, Project Synapse Tease + Sponsor Thanks

Luke Lohr sits down with Circana's Executive Director and video game industry analyst Mat Piscatella for a data-driven look at the state of gaming in 2026. Despite U.S. video game spending approaching $61 billion, the industry feels unsettled. Piscatella explains why record revenue can coexist with market instability, why player growth has plateaued, and how attention is increasingly concentrated in a handful of live-service “black hole” titles. The conversation explores: • The impact of component and RAM shortages on console pricing and availability • Why subscription growth is now focused on revenue per user rather than user growth • Xbox hardware performance and Game Pass strategy • The growing divide between affluent and price-sensitive players • Why Grand Theft Auto VI could be critical to console momentum • Discoverability challenges and storefront power • Why CCU charts don't tell the full story

Tickets for AIEi Miami and AIE Europe are live, with first wave speakers announced!From pioneering software-defined networking to backing many of the most aggressive AI model companies of this cycle, Martin Casado and Sarah Wang sit at the center of the capital, compute, and talent arms race reshaping the tech industry. As partners at a16z investing across infrastructure and growth, they've watched venture and growth blur, model labs turn dollars into capability at unprecedented speed, and startups raise nine-figure rounds before monetization.Martin and Sarah join us to unpack the new financing playbook for AI: why today's rounds are really compute contracts in disguise, how the “raise → train → ship → raise bigger” flywheel works, and whether foundation model companies can outspend the entire app ecosystem built on top of them. They also share what's underhyped (boring enterprise software), what's overheated (talent wars and compensation spirals), and the two radically different futures they see for AI's market structure.We discuss:* Martin's “two futures” fork: infinite fragmentation and new software categories vs. a small oligopoly of general models that consume everything above them* The capital flywheel: how model labs translate funding directly into capability gains, then into revenue growth measured in weeks, not years* Why venture and growth have merged: $100M–$1B hybrid rounds, strategic investors, compute negotiations, and complex deal structures* The AGI vs. product tension: allocating scarce GPUs between long-term research and near-term revenue flywheels* Whether frontier labs can out-raise and outspend the entire app ecosystem built on top of their APIs* Why today's talent wars ($10M+ comp packages, $B acqui-hires) are breaking early-stage founder math* Cursor as a case study: building up from the app layer while training down into your own models* Why “boring” enterprise software may be the most underinvested opportunity in the AI mania* Hardware and robotics: why the ChatGPT moment hasn't yet arrived for robots and what would need to change* World Labs and generative 3D: bringing the marginal cost of 3D scene creation down by orders of magnitude* Why public AI discourse is often wildly disconnected from boardroom reality and how founders should navigate the noiseShow Notes:* “Where Value Will Accrue in AI: Martin Casado & Sarah Wang” - a16z show* “Jack Altman & Martin Casado on the Future of Venture Capital”* World Labs—Martin Casado• LinkedIn: https://www.linkedin.com/in/martincasado/• X: https://x.com/martin_casadoSarah Wang• LinkedIn: https://www.linkedin.com/in/sarah-wang-59b96a7• X: https://x.com/sarahdingwanga16z• https://a16z.com/Timestamps00:00:00 – Intro: Live from a16z00:01:20 – The New AI Funding Model: Venture + Growth Collide00:03:19 – Circular Funding, Demand & “No Dark GPUs”00:05:24 – Infrastructure vs Apps: The Lines Blur00:06:24 – The Capital Flywheel: Raise → Train → Ship → Raise Bigger00:09:39 – Can Frontier Labs Outspend the Entire App Ecosystem?00:11:24 – Character AI & The AGI vs Product Dilemma00:14:39 – Talent Wars, $10M Engineers & Founder Anxiety00:17:33 – What's Underinvested? The Case for “Boring” Software00:19:29 – Robotics, Hardware & Why It's Hard to Win00:22:42 – Custom ASICs & The $1B Training Run Economics00:24:23 – American Dynamism, Geography & AI Power Centers00:26:48 – How AI Is Changing the Investor Workflow (Claude Cowork)00:29:12 – Two Futures of AI: Infinite Expansion or Oligopoly?00:32:48 – If You Can Raise More Than Your Ecosystem, You Win00:34:27 – Are All Tasks AGI-Complete? Coding as the Test Case00:38:55 – Cursor & The Power of the App Layer00:44:05 – World Labs, Spatial Intelligence & 3D Foundation Models00:47:20 – Thinking Machines, Founder Drama & Media Narratives00:52:30 – Where Long-Term Power Accrues in the AI StackTranscriptLatent.Space - Inside AI's $10B+ Capital Flywheel — Martin Casado & Sarah Wang of a16z[00:00:00] Welcome to Latent Space (Live from a16z) + Meet the Guests[00:00:00] Alessio: Hey everyone. Welcome to the Latent Space podcast, live from a 16 z. Uh, this is Alessio founder Kernel Lance, and I'm joined by Twix, editor of Latent Space.[00:00:08] swyx: Hey, hey, hey. Uh, and we're so glad to be on with you guys. Also a top AI podcast, uh, Martin Cado and Sarah Wang. Welcome, very[00:00:16] Martin Casado: happy to be here and welcome.[00:00:17] swyx: Yes, uh, we love this office. We love what you've done with the place. Uh, the new logo is everywhere now. It's, it's still getting, takes a while to get used to, but it reminds me of like sort of a callback to a more ambitious age, which I think is kind of[00:00:31] Martin Casado: definitely makes a statement.[00:00:33] swyx: Yeah.[00:00:34] Martin Casado: Not quite sure what that statement is, but it makes a statement.[00:00:37] swyx: Uh, Martin, I go back with you to Netlify.[00:00:40] Martin Casado: Yep.[00:00:40] swyx: Uh, and, uh, you know, you create a software defined networking and all, all that stuff people can read up on your background. Yep. Sarah, I'm newer to you. Uh, you, you sort of started working together on AI infrastructure stuff.[00:00:51] Sarah Wang: That's right. Yeah. Seven, seven years ago now.[00:00:53] Martin Casado: Best growth investor in the entire industry.[00:00:55] swyx: Oh, say[00:00:56] Martin Casado: more hands down there is, there is. [00:01:00] I mean, when it comes to AI companies, Sarah, I think has done the most kind of aggressive, um, investment thesis around AI models, right? So, worked for Nom Ja, Mira Ia, FEI Fey, and so just these frontier, kind of like large AI models.[00:01:15] I think, you know, Sarah's been the, the broadest investor. Is that fair?[00:01:20] Venture vs. Growth in the Frontier Model Era[00:01:20] Sarah Wang: No, I, well, I was gonna say, I think it's been a really interesting tag, tag team actually just ‘cause the, a lot of these big C deals, not only are they raising a lot of money, um, it's still a tech founder bet, which obviously is inherently early stage.[00:01:33] But the resources,[00:01:36] Martin Casado: so many, I[00:01:36] Sarah Wang: was gonna say the resources one, they just grow really quickly. But then two, the resources that they need day one are kind of growth scale. So I, the hybrid tag team that we have is. Quite effective, I think,[00:01:46] Martin Casado: what is growth these days? You know, you don't wake up if it's less than a billion or like, it's, it's actually, it's actually very like, like no, it's a very interesting time in investing because like, you know, take like the character around, right?[00:01:59] These tend to [00:02:00] be like pre monetization, but the dollars are large enough that you need to have a larger fund and the analysis. You know, because you've got lots of users. ‘cause this stuff has such high demand requires, you know, more of a number sophistication. And so most of these deals, whether it's US or other firms on these large model companies, are like this hybrid between venture growth.[00:02:18] Sarah Wang: Yeah. Total. And I think, you know, stuff like BD for example, you wouldn't usually need BD when you were seed stage trying to get market biz Devrel. Biz Devrel, exactly. Okay. But like now, sorry, I'm,[00:02:27] swyx: I'm not familiar. What, what, what does biz Devrel mean for a venture fund? Because I know what biz Devrel means for a company.[00:02:31] Sarah Wang: Yeah.[00:02:32] Compute Deals, Strategics, and the ‘Circular Funding' Question[00:02:32] Sarah Wang: You know, so a, a good example is, I mean, we talk about buying compute, but there's a huge negotiation involved there in terms of, okay, do you get equity for the compute? What, what sort of partner are you looking at? Is there a go-to market arm to that? Um, and these are just things on this scale, hundreds of millions, you know, maybe.[00:02:50] Six months into the inception of a company, you just wouldn't have to negotiate these deals before.[00:02:54] Martin Casado: Yeah. These large rounds are very complex now. Like in the past, if you did a series A [00:03:00] or a series B, like whatever, you're writing a 20 to a $60 million check and you call it a day. Now you normally have financial investors and strategic investors, and then the strategic portion always still goes with like these kind of large compute contracts, which can take months to do.[00:03:13] And so it's, it's very different ties. I've been doing this for 10 years. It's the, I've never seen anything like this.[00:03:19] swyx: Yeah. Do you have worries about the circular funding from so disease strategics?[00:03:24] Martin Casado: I mean, listen, as long as the demand is there, like the demand is there. Like the problem with the internet is the demand wasn't there.[00:03:29] swyx: Exactly. All right. This, this is like the, the whole pyramid scheme bubble thing, where like, as long as you mark to market on like the notional value of like, these deals, fine, but like once it starts to chip away, it really Well[00:03:41] Martin Casado: no, like as, as, as, as long as there's demand. I mean, you know, this, this is like a lot of these sound bites have already become kind of cliches, but they're worth saying it.[00:03:47] Right? Like during the internet days, like we were. Um, raising money to put fiber in the ground that wasn't used. And that's a problem, right? Because now you actually have a supply overhang.[00:03:58] swyx: Mm-hmm.[00:03:59] Martin Casado: And even in the, [00:04:00] the time of the, the internet, like the supply and, and bandwidth overhang, even as massive as it was in, as massive as the crash was only lasted about four years.[00:04:09] But we don't have a supply overhang. Like there's no dark GPUs, right? I mean, and so, you know, circular or not, I mean, you know, if, if someone invests in a company that, um. You know, they'll actually use the GPUs. And on the other side of it is the, is the ask for customer. So I I, I think it's a different time.[00:04:25] Sarah Wang: I think the other piece, maybe just to add onto this, and I'm gonna quote Martine in front of him, but this is probably also a unique time in that. For the first time, you can actually trace dollars to outcomes. Yeah, right. Provided that scaling laws are, are holding, um, and capabilities are actually moving forward.[00:04:40] Because if you can put translate dollars into capabilities, uh, a capability improvement, there's demand there to martine's point. But if that somehow breaks, you know, obviously that's an important assumption in this whole thing to make it work. But you know, instead of investing dollars into sales and marketing, you're, you're investing into r and d to get to the capability, um, you know, increase.[00:04:59] And [00:05:00] that's sort of been the demand driver because. Once there's an unlock there, people are willing to pay for it.[00:05:05] Alessio: Yeah.[00:05:06] Blurring Lines: Models as Infra + Apps, and the New Fundraising Flywheel[00:05:06] Alessio: Is there any difference in how you built the portfolio now that some of your growth companies are, like the infrastructure of the early stage companies, like, you know, OpenAI is now the same size as some of the cloud providers were early on.[00:05:16] Like what does that look like? Like how much information can you feed off each other between the, the two?[00:05:24] Martin Casado: There's so many lines that are being crossed right now, or blurred. Right. So we already talked about venture and growth. Another one that's being blurred is between infrastructure and apps, right? So like what is a model company?[00:05:35] Mm-hmm. Like, it's clearly infrastructure, right? Because it's like, you know, it's doing kind of core r and d. It's a horizontal platform, but it's also an app because it's um, uh, touches the users directly. And then of course. You know, the, the, the growth of these is just so high. And so I actually think you're just starting to see a, a, a new financing strategy emerge and, you know, we've had to adapt as a result of that.[00:05:59] And [00:06:00] so there's been a lot of changes. Um, you're right that these companies become platform companies very quickly. You've got ecosystem build out. So none of this is necessarily new, but the timescales of which it's happened is pretty phenomenal. And the way we'd normally cut lines before is blurred a little bit, but.[00:06:16] But that, that, that said, I mean, a lot of it also just does feel like things that we've seen in the past, like cloud build out the internet build out as well.[00:06:24] Sarah Wang: Yeah. Um, yeah, I think it's interesting, uh, I don't know if you guys would agree with this, but it feels like the emerging strategy is, and this builds off of your other question, um.[00:06:33] You raise money for compute, you pour that or you, you pour the money into compute, you get some sort of breakthrough. You funnel the breakthrough into your vertically integrated application. That could be chat GBT, that could be cloud code, you know, whatever it is. You massively gain share and get users.[00:06:49] Maybe you're even subsidizing at that point. Um, depending on your strategy. You raise money at the peak momentum and then you repeat, rinse and repeat. Um, and so. And that wasn't [00:07:00] true even two years ago, I think. Mm-hmm. And so it's sort of to your, just tying it to fundraising strategy, right? There's a, and hiring strategy.[00:07:07] All of these are tied, I think the lines are blurring even more today where everyone is, and they, but of course these companies all have API businesses and so they're these, these frenemy lines that are getting blurred in that a lot of, I mean, they have billions of dollars of API revenue, right? And so there are customers there.[00:07:23] But they're competing on the app layer.[00:07:24] Martin Casado: Yeah. So this is a really, really important point. So I, I would say for sure, venture and growth, that line is blurry app and infrastructure. That line is blurry. Um, but I don't think that that changes our practice so much. But like where the very open questions are like, does this layer in the same way.[00:07:43] Compute traditionally has like during the cloud is like, you know, like whatever, somebody wins one layer, but then another whole set of companies wins another layer. But that might not, might not be the case here. It may be the case that you actually can't verticalize on the token string. Like you can't build an app like it, it necessarily goes down just because there are no [00:08:00] abstractions.[00:08:00] So those are kinda the bigger existential questions we ask. Another thing that is very different this time than in the history of computer sciences is. In the past, if you raised money, then you basically had to wait for engineering to catch up. Which famously doesn't scale like the mythical mammoth. It take a very long time.[00:08:18] But like that's not the case here. Like a model company can raise money and drop a model in a, in a year, and it's better, right? And, and it does it with a team of 20 people or 10 people. So this type of like money entering a company and then producing something that has demand and growth right away and using that to raise more money is a very different capital flywheel than we've ever seen before.[00:08:39] And I think everybody's trying to understand what the consequences are. So I think it's less about like. Big companies and growth and this, and more about these more systemic questions that we actually don't have answers to.[00:08:49] Alessio: Yeah, like at Kernel Labs, one of our ideas is like if you had unlimited money to spend productively to turn tokens into products, like the whole early stage [00:09:00] market is very different because today you're investing X amount of capital to win a deal because of price structure and whatnot, and you're kind of pot committing.[00:09:07] Yeah. To a certain strategy for a certain amount of time. Yeah. But if you could like iteratively spin out companies and products and just throw, I, I wanna spend a million dollar of inference today and get a product out tomorrow.[00:09:18] swyx: Yeah.[00:09:19] Alessio: Like, we should get to the point where like the friction of like token to product is so low that you can do this and then you can change the Right, the early stage venture model to be much more iterative.[00:09:30] And then every round is like either 100 k of inference or like a hundred million from a 16 Z. There's no, there's no like $8 million C round anymore. Right.[00:09:38] When Frontier Labs Outspend the Entire App Ecosystem[00:09:38] Martin Casado: But, but, but, but there's a, there's a, the, an industry structural question that we don't know the answer to, which involves the frontier models, which is, let's take.[00:09:48] Anthropic it. Let's say Anthropic has a state-of-the-art model that has some large percentage of market share. And let's say that, uh, uh, uh, you know, uh, a company's building smaller models [00:10:00] that, you know, use the bigger model in the background, open 4.5, but they add value on top of that. Now, if Anthropic can raise three times more.[00:10:10] Every subsequent round, they probably can raise more money than the entire app ecosystem that's built on top of it. And if that's the case, they can expand beyond everything built on top of it. It's like imagine like a star that's just kind of expanding, so there could be a systemic. There could be a, a systemic situation where the soda models can raise so much money that they can out pay anybody that bills on top of ‘em, which would be something I don't think we've ever seen before just because we were so bottlenecked in engineering, and this is a very open question.[00:10:41] swyx: Yeah. It's, it is almost like bitter lesson applied to the startup industry.[00:10:45] Martin Casado: Yeah, a hundred percent. It literally becomes an issue of like raise capital, turn that directly into growth. Use that to raise three times more. Exactly. And if you can keep doing that, you literally can outspend any company that's built the, not any company.[00:10:57] You can outspend the aggregate of companies on top of [00:11:00] you and therefore you'll necessarily take their share, which is crazy.[00:11:02] swyx: Would you say that kind of happens in character? Is that the, the sort of postmortem on. What happened?[00:11:10] Sarah Wang: Um,[00:11:10] Martin Casado: no.[00:11:12] Sarah Wang: Yeah, because I think so,[00:11:13] swyx: I mean the actual postmortem is, he wanted to go back to Google.[00:11:15] Exactly. But like[00:11:18] Martin Casado: that's another difference that[00:11:19] Sarah Wang: you said[00:11:21] Martin Casado: it. We should talk, we should actually talk about that.[00:11:22] swyx: Yeah,[00:11:22] Sarah Wang: that's[00:11:23] swyx: Go for it. Take it. Take,[00:11:23] Sarah Wang: yeah.[00:11:24] Character.AI, Founder Goals (AGI vs Product), and GPU Allocation Tradeoffs[00:11:24] Sarah Wang: I was gonna say, I think, um. The, the, the character thing raises actually a different issue, which actually the Frontier Labs will face as well. So we'll see how they handle it.[00:11:34] But, um, so we invest in character in January, 2023, which feels like eons ago, I mean, three years ago. Feels like lifetimes ago. But, um, and then they, uh, did the IP licensing deal with Google in August, 2020. Uh, four. And so, um, you know, at the time, no, you know, he's talked publicly about this, right? He wanted to Google wouldn't let him put out products in the world.[00:11:56] That's obviously changed drastically. But, um, he went to go do [00:12:00] that. Um, but he had a product attached. The goal was, I mean, it's Nome Shair, he wanted to get to a GI. That was always his personal goal. But, you know, I think through collecting data, right, and this sort of very human use case, that the character product.[00:12:13] Originally was and still is, um, was one of the vehicles to do that. Um, I think the real reason that, you know. I if you think about the, the stress that any company feels before, um, you ultimately going one way or the other is sort of this a GI versus product. Um, and I think a lot of the big, I think, you know, opening eyes, feeling that, um, anthropic if they haven't started, you know, felt it, certainly given the success of their products, they may start to feel that soon.[00:12:39] And the real. I think there's real trade-offs, right? It's like how many, when you think about GPUs, that's a limited resource. Where do you allocate the GPUs? Is it toward the product? Is it toward new re research? Right? Is it, or long-term research, is it toward, um, n you know, near to midterm research? And so, um, in a case where you're resource constrained, um, [00:13:00] of course there's this fundraising game you can play, right?[00:13:01] But the fund, the market was very different back in 2023 too. Um. I think the best researchers in the world have this dilemma of, okay, I wanna go all in on a GI, but it's the product usage revenue flywheel that keeps the revenue in the house to power all the GPUs to get to a GI. And so it does make, um, you know, I think it sets up an interesting dilemma for any startup that has trouble raising up until that level, right?[00:13:27] And certainly if you don't have that progress, you can't continue this fly, you know, fundraising flywheel.[00:13:32] Martin Casado: I would say that because, ‘cause we're keeping track of all of the things that are different, right? Like, you know, venture growth and uh, app infra and one of the ones is definitely the personalities of the founders.[00:13:45] It's just very different this time I've been. Been doing this for a decade and I've been doing startups for 20 years. And so, um, I mean a lot of people start this to do a GI and we've never had like a unified North star that I recall in the same [00:14:00] way. Like people built companies to start companies in the past.[00:14:02] Like that was what it was. Like I would create an internet company, I would create infrastructure company, like it's kind of more engineering builders and this is kind of a different. You know, mentality. And some companies have harnessed that incredibly well because their direction is so obviously on the path to what somebody would consider a GI, but others have not.[00:14:20] And so like there is always this tension with personnel. And so I think we're seeing more kind of founder movement.[00:14:27] Sarah Wang: Yeah.[00:14:27] Martin Casado: You know, as a fraction of founders than we've ever seen. I mean, maybe since like, I don't know the time of like Shockly and the trade DUR aid or something like that. Way back in the beginning of the industry, I, it's a very, very.[00:14:38] Unusual time of personnel.[00:14:39] Sarah Wang: Totally.[00:14:40] Talent Wars, Mega-Comp, and the Rise of Acquihire M&A[00:14:40] Sarah Wang: And it, I think it's exacerbated by the fact that talent wars, I mean, every industry has talent wars, but not at this magnitude, right? No. Yeah. Very rarely can you see someone get poached for $5 billion. That's hard to compete with. And then secondly, if you're a founder in ai, you could fart and it would be on the front page of, you know, the information these days.[00:14:59] And so there's [00:15:00] sort of this fishbowl effect that I think adds to the deep anxiety that, that these AI founders are feeling.[00:15:06] Martin Casado: Hmm.[00:15:06] swyx: Uh, yes. I mean, just on, uh, briefly comment on the founder, uh, the sort of. Talent wars thing. I feel like 2025 was just like a blip. Like I, I don't know if we'll see that again.[00:15:17] ‘cause meta built the team. Like, I don't know if, I think, I think they're kind of done and like, who's gonna pay more than meta? I, I don't know.[00:15:23] Martin Casado: I, I agree. So it feels so, it feel, it feels this way to me too. It's like, it is like, basically Zuckerberg kind of came out swinging and then now he's kind of back to building.[00:15:30] Yeah,[00:15:31] swyx: yeah. You know, you gotta like pay up to like assemble team to rush the job, whatever. But then now, now you like you, you made your choices and now they got a ship.[00:15:38] Martin Casado: I mean, the, the o other side of that is like, you know, like we're, we're actually in the job hiring market. We've got 600 people here. I hire all the time.[00:15:44] I've got three open recs if anybody's interested, that's listening to this for investor. Yeah, on, on the team, like on the investing side of the team, like, and, um, a lot of the people we talk to have acting, you know, active, um, offers for 10 million a year or something like that. And like, you know, and we pay really, [00:16:00] really well.[00:16:00] And just to see what's out on the market is really, is really remarkable. And so I would just say it's actually, so you're right, like the really flashy one, like I will get someone for, you know, a billion dollars, but like the inflated, um, uh, trickles down. Yeah, it is still very active today. I mean,[00:16:18] Sarah Wang: yeah, you could be an L five and get an offer in the tens of millions.[00:16:22] Okay. Yeah. Easily. Yeah. It's so I think you're right that it felt like a blip. I hope you're right. Um, but I think it's been, the steady state is now, I think got pulled up. Yeah. Yeah. I'll pull up for[00:16:31] Martin Casado: sure. Yeah.[00:16:32] Alessio: Yeah. And I think that's breaking the early stage founder math too. I think before a lot of people would be like, well, maybe I should just go be a founder instead of like getting paid.[00:16:39] Yeah. 800 KA million at Google. But if I'm getting paid. Five, 6 million. That's different but[00:16:45] Martin Casado: on. But on the other hand, there's more strategic money than we've ever seen historically, right? Mm-hmm. And so, yep. The economics, the, the, the, the calculus on the economics is very different in a number of ways. And, uh, it's crazy.[00:16:58] It's cra it's causing like a, [00:17:00] a, a, a ton of change in confusion in the market. Some very positive, sub negative, like, so for example, the other side of the, um. The co-founder, like, um, acquisition, you know, mark Zuckerberg poaching someone for a lot of money is like, we were actually seeing historic amount of m and a for basically acquihires, right?[00:17:20] That you like, you know, really good outcomes from a venture perspective that are effective acquihires, right? So I would say it's probably net positive from the investment standpoint, even though it seems from the headlines to be very disruptive in a negative way.[00:17:33] Alessio: Yeah.[00:17:33] What's Underfunded: Boring Software, Robotics Skepticism, and Custom Silicon Economics[00:17:33] Alessio: Um, let's talk maybe about what's not being invested in, like maybe some interesting ideas that you would see more people build or it, it seems in a way, you know, as ycs getting more popular, it's like access getting more popular.[00:17:47] There's a startup school path that a lot of founders take and they know what's hot in the VC circles and they know what gets funded. Uh, and there's maybe not as much risk appetite for. Things outside of that. Um, I'm curious if you feel [00:18:00] like that's true and what are maybe, uh, some of the areas, uh, that you think are under discussed?[00:18:06] Martin Casado: I mean, I actually think that we've taken our eye off the ball in a lot of like, just traditional, you know, software companies. Um, so like, I mean. You know, I think right now there's almost a barbell, like you're like the hot thing on X, you're deep tech.[00:18:21] swyx: Mm-hmm.[00:18:22] Martin Casado: Right. But I, you know, I feel like there's just kind of a long, you know, list of like good.[00:18:28] Good companies that will be around for a long time in very large markets. Say you're building a database, you know, say you're building, um, you know, kind of monitoring or logging or tooling or whatever. There's some good companies out there right now, but like, they have a really hard time getting, um, the attention of investors.[00:18:43] And it's almost become a meme, right? Which is like, if you're not basically growing from zero to a hundred in a year, you're not interesting, which is just, is the silliest thing to say. I mean, think of yourself as like an introvert person, like, like your personal money, right? Mm-hmm. So. Your personal money, will you put it in the stock market at 7% or you put it in this company growing five x in a very large [00:19:00] market?[00:19:00] Of course you can put it in the company five x. So it's just like we say these stupid things, like if you're not going from zero to a hundred, but like those, like who knows what the margins of those are mean. Clearly these are good investments. True for anybody, right? True. Like our LPs want whatever.[00:19:12] Three x net over, you know, the life cycle of a fund, right? So a, a company in a big market growing five X is a great investment. We'd, everybody would be happy with these returns, but we've got this kind of mania on these, these strong growths. And so I would say that that's probably the most underinvested sector.[00:19:28] Right now.[00:19:29] swyx: Boring software, boring enterprise software.[00:19:31] Martin Casado: Traditional. Really good company.[00:19:33] swyx: No, no AI here.[00:19:34] Martin Casado: No. Like boring. Well, well, the AI of course is pulling them into use cases. Yeah, but that's not what they're, they're not on the token path, right? Yeah. Let's just say that like they're software, but they're not on the token path.[00:19:41] Like these are like they're great investments from any definition except for like random VC on Twitter saying VC on x, saying like, it's not growing fast enough. What do you[00:19:52] Sarah Wang: think? Yeah, maybe I'll answer a slightly different. Question, but adjacent to what you asked, um, which is maybe an area that we're not, uh, investing [00:20:00] right now that I think is a question and we're spending a lot of time in regardless of whether we pull the trigger or not.[00:20:05] Um, and it would probably be on the hardware side, actually. Robotics, right? And the robotics side. Robotics. Right. Which is, it's, I don't wanna say that it's not getting funding ‘cause it's clearly, uh, it's, it's sort of non-consensus to almost not invest in robotics at this point. But, um, we spent a lot of time in that space and I think for us, we just haven't seen the chat GPT moment.[00:20:22] Happen on the hardware side. Um, and the funding going into it feels like it's already. Taking that for granted.[00:20:30] Martin Casado: Yeah. Yeah. But we also went through the drone, you know, um, there's a zip line right, right out there. What's that? Oh yeah, there's a zip line. Yeah. What the drone, what the av And like one of the takeaways is when it comes to hardware, um, most companies will end up verticalizing.[00:20:46] Like if you're. If you're investing in a robot company for an A for agriculture, you're investing in an ag company. ‘cause that's the competition and that's surprising. And that's supply chain. And if you're doing it for mining, that's mining. And so the ad team does a lot of that type of stuff ‘cause they actually set up to [00:21:00] diligence that type of work.[00:21:01] But for like horizontal technology investing, there's very little when it comes to robots just because it's so fit for, for purpose. And so we kinda like to look at software. Solutions or horizontal solutions like applied intuition. Clearly from the AV wave deep map, clearly from the AV wave, I would say scale AI was actually a horizontal one for That's fair, you know, for robotics early on.[00:21:23] And so that sort of thing we're very, very interested. But the actual like robot interacting with the world is probably better for different team. Agree.[00:21:30] Alessio: Yeah, I'm curious who these teams are supposed to be that invest in them. I feel like everybody's like, yeah, robotics, it's important and like people should invest in it.[00:21:38] But then when you look at like the numbers, like the capital requirements early on versus like the moment of, okay, this is actually gonna work. Let's keep investing. That seems really hard to predict in a way that is not,[00:21:49] Martin Casado: I think co, CO two, kla, gc, I mean these are all invested in in Harvard companies. He just, you know, and [00:22:00] listen, I mean, it could work this time for sure.[00:22:01] Right? I mean if Elon's doing it, he's like, right. Just, just the fact that Elon's doing it means that there's gonna be a lot of capital and a lot of attempts for a long period of time. So that alone maybe suggests that we should just be investing in robotics just ‘cause you have this North star who's Elon with a humanoid and that's gonna like basically willing into being an industry.[00:22:17] Um, but we've just historically found like. We're a huge believer that this is gonna happen. We just don't feel like we're in a good position to diligence these things. ‘cause again, robotics companies tend to be vertical. You really have to understand the market they're being sold into. Like that's like that competitive equilibrium with a human being is what's important.[00:22:34] It's not like the core tech and like we're kind of more horizontal core tech type investors. And this is Sarah and I. Yeah, the ad team is different. They can actually do these types of things.[00:22:42] swyx: Uh, just to clarify, AD stands for[00:22:44] Martin Casado: American Dynamism.[00:22:45] swyx: Alright. Okay. Yeah, yeah, yeah. Uh, I actually, I do have a related question that, first of all, I wanna acknowledge also just on the, on the chip side.[00:22:51] Yeah. I, I recall a podcast that where you were on, i, I, I think it was the a CC podcast, uh, about two or three years ago where you, where you suddenly said [00:23:00] something, which really stuck in my head about how at some point, at some point kind of scale it makes sense to. Build a custom aic Yes. For per run.[00:23:07] Martin Casado: Yes.[00:23:07] It's crazy. Yeah.[00:23:09] swyx: We're here and I think you, you estimated 500 billion, uh, something.[00:23:12] Martin Casado: No, no, no. A billion, a billion dollar training run of $1 billion training run. It makes sense to actually do a custom meic if you can do it in time. The question now is timelines. Yeah, but not money because just, just, just rough math.[00:23:22] If it's a billion dollar training. Then the inference for that model has to be over a billion, otherwise it won't be solvent. So let's assume it's, if you could save 20%, which you could save much more than that with an ASIC 20%, that's $200 million. You can tape out a chip for $200 million. Right? So now you can literally like justify economically, not timeline wise.[00:23:41] That's a different issue. An ASIC per model, which[00:23:44] swyx: is because that, that's how much we leave on the table every single time. We, we, we do like generic Nvidia.[00:23:48] Martin Casado: Exactly. Exactly. No, it, it is actually much more than that. You could probably get, you know, a factor of two, which would be 500 million.[00:23:54] swyx: Typical MFU would be like 50.[00:23:55] Yeah, yeah. And that's good.[00:23:57] Martin Casado: Exactly. Yeah. Hundred[00:23:57] swyx: percent. Um, so, so, yeah, and I mean, and I [00:24:00] just wanna acknowledge like, here we are in, in, in 2025 and opening eyes confirming like Broadcom and all the other like custom silicon deals, which is incredible. I, I think that, uh, you know, speaking about ad there's, there's a really like interesting tie in that obviously you guys are hit on, which is like these sort, this sort of like America first movement or like sort of re industrialized here.[00:24:17] Yeah. Uh, move TSMC here, if that's possible. Um, how much overlap is there from ad[00:24:23] Martin Casado: Yeah.[00:24:23] swyx: To, I guess, growth and, uh, investing in particularly like, you know, US AI companies that are strongly bounded by their compute.[00:24:32] Martin Casado: Yeah. Yeah. So I mean, I, I would view, I would view AD as more as a market segmentation than like a mission, right?[00:24:37] So the market segmentation is, it has kind of regulatory compliance issues or government, you know, sale or it deals with like hardware. I mean, they're just set up to, to, to, to, to. To diligence those types of companies. So it's a more of a market segmentation thing. I would say the entire firm. You know, which has been since it is been intercepted, you know, has geographical biases, right?[00:24:58] I mean, for the longest time we're like, you [00:25:00] know, bay Area is gonna be like, great, where the majority of the dollars go. Yeah. And, and listen, there, there's actually a lot of compounding effects for having a geographic bias. Right. You know, everybody's in the same place. You've got an ecosystem, you're there, you've got presence, you've got a network.[00:25:12] Um, and, uh, I mean, I would say the Bay area's very much back. You know, like I, I remember during pre COVID, like it was like almost Crypto had kind of. Pulled startups away. Miami from the Bay Area. Miami, yeah. Yeah. New York was, you know, because it's so close to finance, came up like Los Angeles had a moment ‘cause it was so close to consumer, but now it's kind of come back here.[00:25:29] And so I would say, you know, we tend to be very Bay area focused historically, even though of course we've asked all over the world. And then I would say like, if you take the ring out, you know, one more, it's gonna be the US of course, because we know it very well. And then one more is gonna be getting us and its allies and Yeah.[00:25:44] And it goes from there.[00:25:45] Sarah Wang: Yeah,[00:25:45] Martin Casado: sorry.[00:25:46] Sarah Wang: No, no. I agree. I think from a, but I think from the intern that that's sort of like where the companies are headquartered. Maybe your questions on supply chain and customer base. Uh, I, I would say our customers are, are, our companies are fairly international from that perspective.[00:25:59] Like they're selling [00:26:00] globally, right? They have global supply chains in some cases.[00:26:03] Martin Casado: I would say also the stickiness is very different.[00:26:05] Sarah Wang: Yeah.[00:26:05] Martin Casado: Historically between venture and growth, like there's so much company building in venture, so much so like hiring the next PM. Introducing the customer, like all of that stuff.[00:26:15] Like of course we're just gonna be stronger where we have our network and we've been doing business for 20 years. I've been in the Bay Area for 25 years, so clearly I'm just more effective here than I would be somewhere else. Um, where I think, I think for some of the later stage rounds, the companies don't need that much help.[00:26:30] They're already kind of pretty mature historically, so like they can kind of be everywhere. So there's kind of less of that stickiness. This is different in the AI time. I mean, Sarah is now the, uh, chief of staff of like half the AI companies in, uh, in the Bay Area right now. She's like, ops Ninja Biz, Devrel, BizOps.[00:26:48] swyx: Are, are you, are you finding much AI automation in your work? Like what, what is your stack.[00:26:53] Sarah Wang: Oh my, in my personal stack.[00:26:54] swyx: I mean, because like, uh, by the way, it's the, the, the reason for this is it is triggering, uh, yeah. We, like, I'm hiring [00:27:00] ops, ops people. Um, a lot of ponders I know are also hiring ops people and I'm just, you know, it's opportunity Since you're, you're also like basically helping out with ops with a lot of companies.[00:27:09] What are people doing these days? Because it's still very manual as far as I can tell.[00:27:13] Sarah Wang: Hmm. Yeah. I think the things that we help with are pretty network based, um, in that. It's sort of like, Hey, how do do I shortcut this process? Well, let's connect you to the right person. So there's not quite an AI workflow for that.[00:27:26] I will say as a growth investor, Claude Cowork is pretty interesting. Yeah. Like for the first time, you can actually get one shot data analysis. Right. Which, you know, if you're gonna do a customer database, analyze a cohort retention, right? That's just stuff that you had to do by hand before. And our team, the other, it was like midnight and the three of us were playing with Claude Cowork.[00:27:47] We gave it a raw file. Boom. Perfectly accurate. We checked the numbers. It was amazing. That was my like, aha moment. That sounds so boring. But you know, that's, that's the kind of thing that a growth investor is like, [00:28:00] you know, slaving away on late at night. Um, done in a few seconds.[00:28:03] swyx: Yeah. You gotta wonder what the whole, like, philanthropic labs, which is like their new sort of products studio.[00:28:10] Yeah. What would that be worth as an independent, uh, startup? You know, like a[00:28:14] Martin Casado: lot.[00:28:14] Sarah Wang: Yeah, true.[00:28:16] swyx: Yeah. You[00:28:16] Martin Casado: gotta hand it to them. They've been executing incredibly well.[00:28:19] swyx: Yeah. I, I mean, to me, like, you know, philanthropic, like building on cloud code, I think, uh, it makes sense to me the, the real. Um, pedal to the metal, whatever the, the, the phrase is, is when they start coming after consumer with, uh, against OpenAI and like that is like red alert at Open ai.[00:28:35] Oh, I[00:28:35] Martin Casado: think they've been pretty clear. They're enterprise focused.[00:28:37] swyx: They have been, but like they've been free. Here's[00:28:40] Martin Casado: care publicly,[00:28:40] swyx: it's enterprise focused. It's coding. Right. Yeah.[00:28:43] AI Labs vs Startups: Disruption, Undercutting & the Innovator's Dilemma[00:28:43] swyx: And then, and, but here's cloud, cloud, cowork, and, and here's like, well, we, uh, they, apparently they're running Instagram ads for Claudia.[00:28:50] I, on, you know, for, for people on, I get them all the time. Right. And so, like,[00:28:54] Martin Casado: uh,[00:28:54] swyx: it, it's kind of like this, the disruption thing of, uh, you know. Mo Open has been doing, [00:29:00] consumer been doing the, just pursuing general intelligence in every mo modality, and here's a topic that only focus on this thing, but now they're sort of undercutting and doing the whole innovator's dilemma thing on like everything else.[00:29:11] Martin Casado: It's very[00:29:11] swyx: interesting.[00:29:12] Martin Casado: Yeah, I mean there's, there's a very open que so for me there's like, do you know that meme where there's like the guy in the path and there's like a path this way? There's a path this way. Like one which way Western man. Yeah. Yeah.[00:29:23] Two Futures for AI: Infinite Market vs AGI Oligopoly[00:29:23] Martin Casado: And for me, like, like all the entire industry kind of like hinges on like two potential futures.[00:29:29] So in, in one potential future, um, the market is infinitely large. There's perverse economies of scale. ‘cause as soon as you put a model out there, like it kind of sublimates and all the other models catch up and like, it's just like software's being rewritten and fractured all over the place and there's tons of upside and it just grows.[00:29:48] And then there's another path which is like, well. Maybe these models actually generalize really well, and all you have to do is train them with three times more money. That's all you have to [00:30:00] do, and it'll just consume everything beyond it. And if that's the case, like you end up with basically an oligopoly for everything, like, you know mm-hmm.[00:30:06] Because they're perfectly general and like, so this would be like the, the a GI path would be like, these are perfectly general. They can do everything. And this one is like, this is actually normal software. The universe is complicated. You've got, and nobody knows the answer.[00:30:18] The Economics Reality Check: Gross Margins, Training Costs & Borrowing Against the Future[00:30:18] Martin Casado: My belief is if you actually look at the numbers of these companies, so generally if you look at the numbers of these companies, if you look at like the amount they're making and how much they, they spent training the last model, they're gross margin positive.[00:30:30] You're like, oh, that's really working. But if you look at like. The current training that they're doing for the next model, their gross margin negative. So part of me thinks that a lot of ‘em are kind of borrowing against the future and that's gonna have to slow down. It's gonna catch up to them at some point in time, but we don't really know.[00:30:47] Sarah Wang: Yeah.[00:30:47] Martin Casado: Does that make sense? Like, I mean, it could be, it could be the case that the only reason this is working is ‘cause they can raise that next round and they can train that next model. ‘cause these models have such a short. Life. And so at some point in time, like, you know, they won't be able to [00:31:00] raise that next round for the next model and then things will kind of converge and fragment again.[00:31:03] But right now it's not.[00:31:04] Sarah Wang: Totally. I think the other, by the way, just, um, a meta point. I think the other lesson from the last three years is, and we talk about this all the time ‘cause we're on this. Twitter X bubble. Um, cool. But, you know, if you go back to, let's say March, 2024, that period, it felt like a, I think an open source model with an, like a, you know, benchmark leading capability was sort of launching on a daily basis at that point.[00:31:27] And, um, and so that, you know, that's one period. Suddenly it's sort of like open source takes over the world. There's gonna be a plethora. It's not an oligopoly, you know, if you fast, you know, if you, if you rewind time even before that GPT-4 was number one for. Nine months, 10 months. It's a long time. Right.[00:31:44] Um, and of course now we're in this era where it feels like an oligopoly, um, maybe some very steady state shifts and, and you know, it could look like this in the future too, but it just, it's so hard to call. And I think the thing that keeps, you know, us up at [00:32:00] night in, in a good way and bad way, is that the capability progress is actually not slowing down.[00:32:06] And so until that happens, right, like you don't know what's gonna look like.[00:32:09] Martin Casado: But I, I would, I would say for sure it's not converged, like for sure, like the systemic capital flows have not converged, meaning right now it's still borrowing against the future to subsidize growth currently, which you can do that for a period of time.[00:32:23] But, but you know, at the end, at some point the market will rationalize that and just nobody knows what that will look like.[00:32:29] Alessio: Yeah.[00:32:29] Martin Casado: Or, or like the drop in price of compute will, will, will save them. Who knows?[00:32:34] Alessio: Yeah. Yeah. I think the models need to ask them to, to specific tasks. You know? It's like, okay, now Opus 4.5 might be a GI at some specific task, and now you can like depreciate the model over a longer time.[00:32:45] I think now, now, right now there's like no old model.[00:32:47] Martin Casado: No, but let, but lemme just change that mental, that's, that used to be my mental model. Lemme just change it a little bit.[00:32:53] Capital as a Weapon vs Task Saturation: Where Real Enterprise Value Gets Built[00:32:53] Martin Casado: If you can raise three times, if you can raise more than the aggregate of anybody that uses your models, that doesn't even matter.[00:32:59] It doesn't [00:33:00] even matter. See what I'm saying? Like, yeah. Yeah. So, so I have an API Business. My API business is 60% margin, or 70% margin, or 80% margin is a high margin business. So I know what everybody is using. If I can raise more money than the aggregate of everybody that's using it, I will consume them whether I'm a GI or not.[00:33:14] And I will know if they're using it ‘cause they're using it. And like, unlike in the past where engineering stops me from doing that.[00:33:21] Alessio: Mm-hmm.[00:33:21] Martin Casado: It is very straightforward. You just train. So I also thought it was kind of like, you must ask the code a GI, general, general, general. But I think there's also just a possibility that the, that the capital markets will just give them the, the, the ammunition to just go after everybody on top of ‘em.[00:33:36] Sarah Wang: I, I do wonder though, to your point, um, if there's a certain task that. Getting marginally better isn't actually that much better. Like we've asked them to it, to, you know, we can call it a GI or whatever, you know, actually, Ali Goi talks about this, like we're already at a GI for a lot of functions in the enterprise.[00:33:50] Um. That's probably those for those tasks, you probably could build very specific companies that focus on just getting as much value out of that task that isn't [00:34:00] coming from the model itself. There's probably a rich enterprise business to be built there. I mean, could be wrong on that, but there's a lot of interesting examples.[00:34:08] So, right, if you're looking the legal profession or, or whatnot, and maybe that's not a great one ‘cause the models are getting better on that front too, but just something where it's a bit saturated, then the value comes from. Services. It comes from implementation, right? It comes from all these things that actually make it useful to the end customer.[00:34:24] Martin Casado: Sorry, what am I, one more thing I think is, is underused in all of this is like, to what extent every task is a GI complete.[00:34:31] Sarah Wang: Mm-hmm.[00:34:32] Martin Casado: Yeah. I code every day. It's so fun.[00:34:35] Sarah Wang: That's a core question. Yeah.[00:34:36] Martin Casado: And like. When I'm talking to these models, it's not just code. I mean, it's everything, right? Like I, you know, like it's,[00:34:43] swyx: it's healthcare.[00:34:44] It's,[00:34:44] Martin Casado: I mean, it's[00:34:44] swyx: Mele,[00:34:45] Martin Casado: but it's every, it is exactly that. Like, yeah, that's[00:34:47] Sarah Wang: great support. Yeah.[00:34:48] Martin Casado: It's everything. Like I'm asking these models to, yeah, to understand compliance. I'm asking these models to go search the web. I'm asking these models to talk about things I know in the history, like it's having a full conversation with me while I, I engineer, and so it could be [00:35:00] the case that like, mm-hmm.[00:35:01] The most a, you know, a GI complete, like I'm not an a GI guy. Like I think that's, you know, but like the most a GI complete model will is win independent of the task. And we don't know the answer to that one either.[00:35:11] swyx: Yeah.[00:35:12] Martin Casado: But it seems to me that like, listen, codex in my experience is for sure better than Opus 4.5 for coding.[00:35:18] Like it finds the hardest bugs that I work in with. Like, it is, you know. The smartest developers. I don't work on it. It's great. Um, but I think Opus 4.5 is actually very, it's got a great bedside manner and it really, and it, it really matters if you're building something very complex because like, it really, you know, like you're, you're, you're a partner and a brainstorming partner for somebody.[00:35:38] And I think we don't discuss enough how every task kind of has that quality.[00:35:42] swyx: Mm-hmm.[00:35:43] Martin Casado: And what does that mean to like capital investment and like frontier models and Submodels? Yeah.[00:35:47] Why “Coding Models” Keep Collapsing into Generalists (Reasoning vs Taste)[00:35:47] Martin Casado: Like what happened to all the special coding models? Like, none of ‘em worked right. So[00:35:51] Alessio: some of them, they didn't even get released.[00:35:53] Magical[00:35:54] Martin Casado: Devrel. There's a whole, there's a whole host. We saw a bunch of them and like there's this whole theory that like, there could be, and [00:36:00] I think one of the conclusions is, is like there's no such thing as a coding model,[00:36:04] Alessio: you know?[00:36:04] Martin Casado: Like, that's not a thing. Like you're talking to another human being and it's, it's good at coding, but like it's gotta be good at everything.[00:36:10] swyx: Uh, minor disagree only because I, I'm pretty like, have pretty high confidence that basically open eye will always release a GPT five and a GT five codex. Like that's the code's. Yeah. The way I call it is one for raisin, one for Tiz. Um, and, and then like someone internal open, it was like, yeah, that's a good way to frame it.[00:36:32] Martin Casado: That's so funny.[00:36:33] swyx: Uh, but maybe it, maybe it collapses down to reason and that's it. It's not like a hundred dimensions doesn't life. Yeah. It's two dimensions. Yeah, yeah, yeah, yeah. Like and exactly. Beside manner versus coding. Yeah.[00:36:43] Martin Casado: Yeah.[00:36:44] swyx: It's, yeah.[00:36:46] Martin Casado: I, I think for, for any, it's hilarious. For any, for anybody listening to this for, for, for, I mean, for you, like when, when you're like coding or using these models for something like that.[00:36:52] Like actually just like be aware of how much of the interaction has nothing to do with coding and it just turns out to be a large portion of it. And so like, you're, I [00:37:00] think like, like the best Soto ish model. You know, it is going to remain very important no matter what the task is.[00:37:06] swyx: Yeah.[00:37:07] What He's Actually Coding: Gaussian Splats, Spark.js & 3D Scene Rendering Demos[00:37:07] swyx: Uh, speaking of coding, uh, I, I'm gonna be cheeky and ask like, what actually are you coding?[00:37:11] Because obviously you, you could code anything and you are obviously a busy investor and a manager of the good. Giant team. Um, what are you calling?[00:37:18] Martin Casado: I help, um, uh, FEFA at World Labs. Uh, it's one of the investments and um, and they're building a foundation model that creates 3D scenes.[00:37:27] swyx: Yeah, we had it on the pod.[00:37:28] Yeah. Yeah,[00:37:28] Martin Casado: yeah. And so these 3D scenes are Gaussian splats, just by the way that kind of AI works. And so like, you can reconstruct a scene better with, with, with radiance feels than with meshes. ‘cause like they don't really have topology. So, so they, they, they produce each. Beautiful, you know, 3D rendered scenes that are Gaussian splats, but the actual industry support for Gaussian splats isn't great.[00:37:50] It's just never, you know, it's always been meshes and like, things like unreal use meshes. And so I work on a open source library called Spark js, which is a. Uh, [00:38:00] a JavaScript rendering layer ready for Gaussian splats. And it's just because, you know, um, you, you, you need that support and, and right now there's kind of a three js moment that's all meshes and so like, it's become kind of the default in three Js ecosystem.[00:38:13] As part of that to kind of exercise the library, I just build a whole bunch of cool demos. So if you see me on X, you see like all my demos and all the world building, but all of that is just to exercise this, this library that I work on. ‘cause it's actually a very tough algorithmics problem to actually scale a library that much.[00:38:29] And just so you know, this is ancient history now, but 30 years ago I paid for undergrad, you know, working on game engines in college in the late nineties. So I've got actually a back and it's very old background, but I actually have a background in this and so a lot of it's fun. You know, but, but the, the, the, the whole goal is just for this rendering library to, to,[00:38:47] Sarah Wang: are you one of the most active contributors?[00:38:49] The, their GitHub[00:38:50] Martin Casado: spark? Yes.[00:38:51] Sarah Wang: Yeah, yeah.[00:38:51] Martin Casado: There's only two of us there, so, yes. No, so by the way, so the, the pri The pri, yeah. Yeah. So the primary developer is a [00:39:00] guy named Andres Quist, who's an absolute genius. He and I did our, our PhDs together. And so like, um, we studied for constant Quas together. It was almost like hanging out with an old friend, you know?[00:39:09] And so like. So he, he's the core, core guy. I did mostly kind of, you know, the side I run venture fund.[00:39:14] swyx: It's amazing. Like five years ago you would not have done any of this. And it brought you back[00:39:19] Martin Casado: the act, the Activ energy, you're still back. Energy was so high because you had to learn all the framework b******t.[00:39:23] Man, I f*****g used to hate that. And so like, now I don't have to deal with that. I can like focus on the algorithmics so I can focus on the scaling and I,[00:39:29] swyx: yeah. Yeah.[00:39:29] LLMs vs Spatial Intelligence + How to Value World Labs' 3D Foundation Model[00:39:29] swyx: And then, uh, I'll observe one irony and then I'll ask a serious investor question, uh, which is like, the irony is FFE actually doesn't believe that LMS can lead us to spatial intelligence.[00:39:37] And here you are using LMS to like help like achieve spatial intelligence. I just see, I see some like disconnect in there.[00:39:45] Martin Casado: Yeah. Yeah. So I think, I think, you know, I think, I think what she would say is LLMs are great to help with coding.[00:39:51] swyx: Yes.[00:39:51] Martin Casado: But like, that's very different than a model that actually like provides, they, they'll never have the[00:39:56] swyx: spatial inte[00:39:56] Martin Casado: issues.[00:39:56] And listen, our brains clearly listen, our brains, brains clearly have [00:40:00] both our, our brains clearly have a language reasoning section and they clearly have a spatial reasoning section. I mean, it's just, you know, these are two pretty independent problems.[00:40:07] swyx: Okay. And you, you, like, I, I would say that the, the one data point I recently had, uh, against it is the DeepMind, uh, IMO Gold, where, so, uh, typically the, the typical answer is that this is where you start going down the neuros symbolic path, right?[00:40:21] Like one, uh, sort of very sort of abstract reasoning thing and one form, formal thing. Um, and that's what. DeepMind had in 2024 with alpha proof, alpha geometry, and now they just use deep think and just extended thinking tokens. And it's one model and it's, and it's in LM.[00:40:36] Martin Casado: Yeah, yeah, yeah, yeah, yeah.[00:40:37] swyx: And so that, that was my indication of like, maybe you don't need a separate system.[00:40:42] Martin Casado: Yeah. So, so let me step back. I mean, at the end of the day, at the end of the day, these things are like nodes in a graph with weights on them. Right. You know, like it can be modeled like if you, if you distill it down. But let me just talk about the two different substrates. Let's, let me put you in a dark room.[00:40:56] Like totally black room. And then let me just [00:41:00] describe how you exit it. Like to your left, there's a table like duck below this thing, right? I mean like the chances that you're gonna like not run into something are very low. Now let me like turn on the light and you actually see, and you can do distance and you know how far something away is and like where it is or whatever.[00:41:17] Then you can do it, right? Like language is not the right primitives to describe. The universe because it's not exact enough. So that's all Faye, Faye is talking about. When it comes to like spatial reasoning, it's like you actually have to know that this is three feet far, like that far away. It is curved.[00:41:37] You have to understand, you know, the, like the actual movement through space.[00:41:40] swyx: Yeah.[00:41:40] Martin Casado: So I do, I listen, I do think at the end of these models are definitely converging as far as models, but there's, there's, there's different representations of problems you're solving. One is language. Which, you know, that would be like describing to somebody like what to do.[00:41:51] And the other one is actually just showing them and the space reasoning is just showing them.[00:41:55] swyx: Yeah, yeah, yeah. Right. Got it, got it. Uh, the, in the investor question was on, on, well labs [00:42:00] is, well, like, how do I value something like this? What, what, what work does the, do you do? I'm just like, Fefe is awesome.[00:42:07] Justin's awesome. And you know, the other two co-founder, co-founders, but like the, the, the tech, everyone's building cool tech. But like, what's the value of the tech? And this is the fundamental question[00:42:16] Martin Casado: of, well, let, let, just like these, let me just maybe give you a rough sketch on the diffusion models. I actually love to hear Sarah because I'm a venture for, you know, so like, ventures always, always like kind of wild west type[00:42:24] swyx: stuff.[00:42:24] You, you, you, you paid a dream and she has to like, actually[00:42:28] Martin Casado: I'm gonna say I'm gonna mar to reality, so I'm gonna say the venture for you. And she can be like, okay, you a little kid. Yeah. So like, so, so these diffusion models literally. Create something for, for almost nothing. And something that the, the world has found to be very valuable in the past, in our real markets, right?[00:42:45] Like, like a 2D image. I mean, that's been an entire market. People value them. It takes a human being a long time to create it, right? I mean, to create a, you know, a, to turn me into a whatever, like an image would cost a hundred bucks in an hour. The inference cost [00:43:00] us a hundredth of a penny, right? So we've seen this with speech in very successful companies.[00:43:03] We've seen this with 2D image. We've seen this with movies. Right? Now, think about 3D scene. I mean, I mean, when's Grand Theft Auto coming out? It's been six, what? It's been 10 years. I mean, how, how like, but hasn't been 10 years.[00:43:14] Alessio: Yeah.[00:43:15] Martin Casado: How much would it cost to like, to reproduce this room in 3D? Right. If you, if you, if you hired somebody on fiber, like in, in any sort of quality, probably 4,000 to $10,000.[00:43:24] And then if you had a professional, probably $30,000. So if you could generate the exact same thing from a 2D image, and we know that these are used and they're using Unreal and they're using Blend, or they're using movies and they're using video games and they're using all. So if you could do that for.[00:43:36] You know, less than a dollar, that's four or five orders of magnitude cheaper. So you're bringing the marginal cost of something that's useful down by three orders of magnitude, which historically have created very large companies. So that would be like the venture kind of strategic dreaming map.[00:43:49] swyx: Yeah.[00:43:50] And, and for listeners, uh, you can do this yourself on your, on your own phone with like. Uh, the marble.[00:43:55] Martin Casado: Yeah. Marble.[00:43:55] swyx: Uh, or but also there's many Nerf apps where you just go on your iPhone and, and do this.[00:43:59] Martin Casado: Yeah. Yeah. [00:44:00] Yeah. And, and in the case of marble though, it would, what you do is you literally give it in.[00:44:03] So most Nerf apps you like kind of run around and take a whole bunch of pictures and then you kind of reconstruct it.[00:44:08] swyx: Yeah.[00:44:08] Martin Casado: Um, things like marble, just that the whole generative 3D space will just take a 2D image and it'll reconstruct all the like, like[00:44:16] swyx: meaning it has to fill in. Uh,[00:44:18] Martin Casado: stuff at the back of the table, under the table, the back, like, like the images, it doesn't see.[00:44:22] So the generator stuff is very different than reconstruction that it fills in the things that you can't see.[00:44:26] swyx: Yeah. Okay.[00:44:26] Sarah Wang: So,[00:44:27] Martin Casado: all right. So now the,[00:44:28] Sarah Wang: no, no. I mean I love that[00:44:29] Martin Casado: the adult[00:44:29] Sarah Wang: perspective. Um, well, no, I was gonna say these are very much a tag team. So we, we started this pod with that, um, premise. And I think this is a perfect question to even build on that further.[00:44:36] ‘cause it truly is, I mean, we're tag teaming all of these together.[00:44:39] Investing in Model Labs, Media Rumors, and the Cursor Playbook (Margins & Going Down-Stack)[00:44:39] Sarah Wang: Um, but I think every investment fundamentally starts with the same. Maybe the same two premises. One is, at this point in time, we actually believe that there are. And of one founders for their particular craft, and they have to be demonstrated in their prior careers, right?[00:44:56] So, uh, we're not investing in every, you know, now the term is NEO [00:45:00] lab, but every foundation model, uh, any, any company, any founder trying to build a foundation model, we're not, um, contrary to popular opinion, we're

Voice used to be AI's forgotten modality — awkward, slow, and fragile. Now it's everywhere. In this reference episode on all things Voice AI, Matt Turck sits down with Neil Zeghidour, a top AI researcher and CEO of Gradium AI (ex-DeepMind/Google, Meta, Kyutai), to cover voice agents, speech-to-speech models, full-duplex conversation, on-device voice, and voice cloning.We unpack what actually changed under the hood — why voice is finally starting to feel natural, and why it may become the default interface for a new generation of AI assistants and devices.Neil breaks down today's dominant “cascaded” voice stack — speech recognition into a text model, then text-to-speech back out — and why it's popular: it's modular and easy to customize. But he argues it has two key downsides: chaining models adds latency, and forcing everything through text strips out paralinguistic signals like tone, stress, and emotion. The next wave, he suggests, is combining cascade-like flexibility with the more natural feel of speech-to-speech and full-duplex conversation.We go deep on full-duplex interaction (ending awkward turn-taking), the hardest unsolved problems (noisy real-world environments and multi-speaker chaos), and the realities of deploying voice at scale — including why models must be compact and when on-device voice is the right approach.Finally, we tackle voice cloning: where it's genuinely useful, what it means for deepfakes and privacy, and why watermarking isn't a silver bullet.If you care about voice agents, real-time AI, and the next generation of human-computer interaction, this is the episode to bookmark.Neil ZeghidourLinkedIn - https://www.linkedin.com/in/neil-zeghidour-a838aaa7/X/Twitter - https://x.com/neilzeghGradiumWebsite - https://gradium.aiX/Twitter - https://x.com/GradiumAIMatt Turck (Managing Director)Blog - https://mattturck.comLinkedIn - https://www.linkedin.com/in/turck/X/Twitter - https://twitter.com/mattturckFirstMarkWebsite - https://firstmark.comX/Twitter - https://twitter.com/FirstMarkCap(00:00) Intro(01:21) Voice AI's big moment — and why we're still early(03:34) Why voice lagged behind text/image/video(06:06) The convergence era: transformers for every modality(07:40) Beyond Her: always-on assistants, wake words, voice-first devices(11:01) Voice vs text: where voice fits (even for coding)(12:56) Neil's origin story: from finance to machine learning(18:35) Neural codecs (SoundStream): compression as the unlock(22:30) Kyutai: open research, small elite teams, moving fast(31:32) Why big labs haven't “won” voice AI4(34:01) On-device voice: where it works, why compact models matter(46:37) The last mile: real-world robustness, pronunciation, uptime(41:35) Benchmarking voice: why metrics fail, how they actually test(47:03) Cascades vs speech-to-speech: trade-offs + what's next(54:05) Hardest frontier: noisy rooms, factories, multi-speaker chaos(1:00:50) New languages + dialects: what transfers, what doesn't(1:02:54 Hardware & compute: why voice isn't a 10,000-GPU game(1:07:27) What data do you need to train voice models?(1:09:02) Deepfakes + privacy: why watermarking isn't a solution(1:12:30) Voice + vision: multimodality, screen awareness, video+audio(1:14:43) Voice cloning vs voice design: where the market goes(1:16:32) Paris/Europe AI: talent density, underdog energy, what's next

Noah Hamman expects semiconductor chips to continue to show strength, focusing on hardware over software as the markets become nervous around valuations. Looking at “picks and shovels,” he likes HVAC companies. He highlights a “nice start” to the year overall for markets and the economy. However, he notes that consumer spending is mostly coming from credit card debt, though delinquencies are staying low. ======== Schwab Network ========Empowering every investor and trader, every market day.Options involve risks and are not suitable for all investors. Before trading, read the Options Disclosure Document. http://bit.ly/2v9tH6DSubscribe to the Market Minute newsletter - https://schwabnetwork.com/subscribeDownload the iOS app - https://apps.apple.com/us/app/schwab-network/id1460719185Download the Amazon Fire Tv App - https://www.amazon.com/TD-Ameritrade-Network/dp/B07KRD76C7Watch on Sling - https://watch.sling.com/1/asset/191928615bd8d47686f94682aefaa007/watchWatch on Vizio - https://www.vizio.com/en/watchfreeplus-exploreWatch on DistroTV - https://www.distro.tv/live/schwab-network/Follow us on X – https://twitter.com/schwabnetworkFollow us on Facebook – https://www.facebook.com/schwabnetworkFollow us on LinkedIn - https://www.linkedin.com/company/schwab-network/About Schwab Network - https://schwabnetwork.com/about

Realities Remixed, formerly know as Cloud Realities, launches a new season exploring the intersection of people, culture, technology, and society. Hosts Dave Chapman, Esmee van de Giessen, and Rob Kernahan unpack 2026's defining trends, from AI and sovereignty to adaptability and automation, offering fresh insight, candid reflections, and forward‑looking conversations shaping the year ahead. TLDR00:20 – Introduction of Realities Remixed02:30 – Why the show evolved?04:50 – Dig in with the team: Predictions for 202606:40 – Macro trends13:00 – Sovereignty 17:40 – Agentic AI22:17 – Human–AI interaction26:06 – Cloud trends30:42 – AI scaling, domain‑specific models35:03 – Adoption lag39:34 – Physical AI43:47 – Quantum computing48:21 – Hardware acceleration50:30 – Cybersecurity52:38 – Season outlook HostsDave Chapman:  https://www.linkedin.com/in/chapmandr/Esmee van de Giessen:  https://www.linkedin.com/in/esmeevandegiessen/Rob Kernahan:  https://www.linkedin.com/in/rob-kernahan/ProductionMarcel van der Burg:  https://www.linkedin.com/in/marcel-vd-burg/Dave Chapman:  https://www.linkedin.com/in/chapmandr/ SoundBen Corbett:  https://www.linkedin.com/in/ben-corbett-3b6a11135/Louis Corbett:   https://www.linkedin.com/in/louis-corbett-087250264/ 'Realities Remixed' is an original podcast from Capgemini

Die RAM-Krise hat den Tech-Bereich fest im Griff – und ein Ende ist nicht in Sicht. Aber trifft es wirklich nur PC-Gamer? Nein. Vom Handy bis zum Smart-Kühlschrank: Der Chip-Mangel ist in der Mitte der Gesellschaft angekommen. In diesem Talk gehen unser Hardware-Urgestein Nils Raettig und Tech-Redakteur Jan Stahnke nicht nur auf die steigenden Preise ein, sondern suchen gemeinsam mit der Community nach Auswegen. Alle Links zum GameStar Podcast und unseren Werbepartnern: https://linktr.ee/gamestarpodcast

On this episode of For Mac Eyes Only: Join Mike and Darren as they discuss Apple's new Creator Studio subscription, stand-alone vs subscription pricing, and who can benefit from using Creator Studio. Mike shares a FMEO Quick Tip for scheduling events and we wrap up with Mike's Essential App pick: Itsytv!

Before the industry caught up… we were already there. Milestone Media didn't just create heroes. They created mirrors. They gave the Blerd community something rare at the time — authentic representation that wasn't a side story, but the main event. Characters like Icon, Static, Rocket, and Hardware didn't ask for permission to exist. They demanded to be seen. That same spirit lives on today. This Tuesday, we sit down with Alim Leggett — writer, artist, art director, husband, father of five, and creator of the indie comic force known as Sweet Pea. A creator carrying the torch forward. A storyteller proving that representation isn't nostalgia — it's evolution. We're talking about: ⚡ How Milestone Media reshaped the cultural and creative landscape ⚡ What representation truly means from the Blerd perspective ⚡ The creation and rise of Sweet Pea ⚡ Why every panel matters — and why our stories matter more than ever This isn't just an interview. This is legacy, impact, and the future — all in one conversation.YouTube ➡️ @blerdseyeview Twitch ➡️ @blerdseyeview1

Black Comic Books with JPenumbraThis Black History Month, we sit down with JPenumbra—TikTok creator, podcast host, and comic journalist—to talk about the state of Black representation in comics and superhero adaptations.From the realities of comic book pre-orders to why Captain America: Brave New World's struggles had nothing to do with Sam Wilson being Black, JP breaks down the systemic issues that keep diverse characters from getting their shot. We also highlight the Black creators shaping today's industry—and why Hardware deserves a screen adaptation immediately.Comics JP RecommendedD'orc – Image ComicsStatic: Season One (2021) – DC/MilestoneKilladelphia – Image ComicsRoots of Madness – Ignition PressCreators JP MentionedRodney BarnesStephanie WilliamsBrandon ThomasDavid F. Walker (retelling of John Henry)Artists JP MentionedSanford GreeneTaurin ClarkeKhary Randolph**************************************************************************This episode is a production of Superhero Ethics, a The Ethical Panda Podcast and part of the TruStory FM Entertainment Podcast Network. Check our our website to find out more about this and our sister podcast Star Wars Generations.We want to hear from you! You can keep up with our latest news, and send us feedback, questions, or comments via social media or email.Email: Matthew@TheEthicalPanda.comFacebook: TheEthicalPandaInstagram: TheEthicalPandaPodcastsTwitter: EthicalPanda77Or you can join jump into the Star Wars Generations and Superhero Ethics channels on the TruStory FM Discord.Want to get access to even more content while supporting the podcast? Become a member! For $5 a month, or $55 a year you get access to bonus episodes and bonus content at the end of most episodes. Sign up on the podcast's main page. You can even give membership as a gift!You can also support our podcasts through our sponsors:Purchase a lightsaber from Level Up Sabers run by friend of the podcast Neighborhood Master AlanUse Audible for audiobooks. Sign up for a one year membership or gift one through this link.Purchase any media discussed this week through our sponsored links.

In this episode, we explore Apple's surprising strategy and success in the AI hardware market, despite its perceived slowness in AI software development. We also discuss how their hardware, particularly the Mac Mini, has become a popular choice for running AI models, leading to unexpected wins for the company.Chapters00:00 Apple's AI Strategy02:19 Upcoming AI Wearables08:32 AirPods and Siri Updates15:18 The Mac Mini Phenomenon19:48 Apple's Future & AI Capex20:43 Special Apple Experience LinksGet the top 40+ AI Models for $20 at AI Box: ⁠⁠https://aibox.aiAI Chat YouTube Channel: https://www.youtube.com/@JaedenSchaferJoin my AI Hustle Community: https://www.skool.com/aihustle

Altium Develop connects your entire electronics product team — from design to supply chain to manufacturing — in one unified platform: https://www.altium.com/develop?utm_source=youtube&utm_medium=video&utm_campaign=ontrack-podcast&utm_content=violet-labs-the-integration-hub-solving-hardware-data-chaos   Hardware engineering teams have long struggled with disconnected software tools — CAD systems that don't talk to PLMs, PLMs that don't sync with MES platforms, and engineers spending hours manually copying BOM data between systems. In this episode of the Altium OnTrack Podcast, host Zach Peterson sits down with Lucy Hoag, founder and CEO of Violet Labs, to explore how her company is tackling this pervasive problem. Lucy shares her journey from astronautical engineering and satellite design to building a no-code integration platform that acts as a centralized hub for all the disparate tools hardware teams rely on every day.   Violet works like a "Zapier for hardware engineering" — connecting mechanical CAD, electrical CAD, PLM, MES, ERP, project management tools, and more through a single middleware platform. Lucy and Zach dig into why native integrations between major software vendors remain rare, the regulatory constraints that have stalled innovation in aerospace, and how Violet's no-code approach removes barriers for non-software engineers. They also discuss Violet's newly launched MCP server, the role of AI in responsible data orchestration, and what's next on the product roadmap.  

Flush de la semana con lo mejor en noticias que salieron en la semanadéjame tu comentario Redes Sociales Oficiales:► https://linktr.ee/DrakSpartanOficialCualquier cosa o situación contactar a Diego Walker:diegowalkercontacto@gmail.comFecha Del Video[17-02-2026]#flush #zam #sony #hdd #intel #memoryram #memoriaram #ia #drakspartan #drak #elflush

Segment 1: Interview with Mathias Katz What if you had enterprise-grade network security protections traveling with your users' laptops? What if it could be built into the laptop, but still stay safe even if the laptop OS and firmware were entirely compromised? Mathias and his company, Byos have built such a thing, and BOY do we have some questions for him. Segment 2: Interview with Wolfgang Goerlich Addressing the nuanced, nefarious threats of AI Sure, we need to worry about AI prompt injection and AI data leakage, but what about the threats to our BRAINS? Seriously, as we start to have daily conversations with this technology, how are they going to shape how we think? What inherent biases in the training, fine tuning, guardrails, or lack of guardrails are going to affect our decisions or how we work? Wolfgang is concerned about this, so he performed a human/AI experiment. With almost 1000 people partaking in the experiment, the results are sure to be intriguing. Segment 3: This week's enterprise security news Finally, in the enterprise security news, survey results on how folks are feeling about openclaw some hidden drama discovered in KEV updates some new KEV tools is AI replacing traditional code scanning tools? remote code execution in notepad no, not notepad++, NOTEPAD.EXE you know, the one that ships preinstalled on Windows the RSAC innovation sandbox finalists dealing with legacy vulnerabilities Don't accept OpenClaw Mac Minis from strangers! All that and more, on this episode of Enterprise Security Weekly. Visit https://www.securityweekly.com/esw for all the latest episodes! Show Notes: https://securityweekly.com/esw-446

As we move into Q1 2026, Brian talks about 3 rooms where he'd like to be a fly on wall to see the blueprints of significant AI companies shaping the markets. SHOW: 1002SHOW TRANSCRIPT: The Cloudcast #1002 TranscriptSHOW VIDEO: https://youtube.com/@TheCloudcastNET NEW TO CLOUD? CHECK OUT OUR OTHER PODCAST - "CLOUDCAST BASICS" A FLY ON THE WALL IN 3 ROOMS IN 2026Room1 - NVIDIA's M&A Room (Chips, Software Stack, Hosting Services, Agentic Tools, etc.)Room 2 - Anthropic's Agentic VisionRoom 3 - TSMC's 2028-2030 PlanningFEEDBACK?Email: show at the cloudcast dot netBluesky: @cloudcastpod.bsky.socialTwitter/X: @cloudcastpodInstagram: @cloudcastpodTikTok: @cloudcastpod

AJ Meyer, CEO of Pickle Robot, isn't betting on general-purpose humanoid robots. Instead, he's a believer in robots and Physical AI which solve specific, high-volume problems. AJ joins Sam and Asad to reveal how he recently secured a nine-figure enterprise contract and why "boring" logistics tasks are the gateway to mass adoption of robots. But with mass adoption's opportunities, so too are there new risks. AJ shares that while physical safety is an important consideration, the cybersecurity risk of a networked robot workforce is what needs the most attention right now. This and a ton more in this week's episode of Topline with Sam Jacobs (CEO @ Pavilion) and Asad Zaman (CEO @ Sales Talent Agency). Thanks for tuning in! Catch new episodes every Sunday Subscribe to Topline Newsletter. Tune into Topline Podcast, the #1 podcast for founders, operators, and investors in B2B tech. Join the free Topline Slack channel to connect with 600+ revenue leaders to keep the conversation going beyond the podcast! Chapters: 00:00 Teaser and Introduction to AJ Meyer 02:53 The Convergence of Physical and Digital AI 05:50 Safety Constraints and the "Acrobat" Robot Disaster 09:19 Mobile Manipulation vs. General Purpose Humanoids 12:47 Cybersecurity Risks in Connected Robot Networks 18:52 AI Surveillance and Authoritarian Risks 28:01 Debunking the Myth of Unskilled Labor 34:54 The Moving Goalposts of AGI 38:19 Solving the Open World Generalization Problem 42:09 Why Foundation Models Need Systems Engineering 48:23 Designing Business Models for Enterprise and Mid-Market 53:20 The Nine-Figure "ChatGPT Moment" for Robotics 58:14 Transferring SaaS Go-To-Market Skills to Hardware 01:03:45 Taste and Judgment as Career Differentiators 01:07:50 Hiring Needs and Closing Thoughts

Episode #589: Microsoft is quietly laying the groundwork for the next era of Xbox — and the signals are finally starting to line up. In this episode, we break down Microsoft's bold Xbox strategy, from next-generation hardware plans to what a longer Xbox Series era really means for players. With reports pointing to a 2027+ next-gen console timeline, Xbox doesn't seem to be in a rush — and that may be the most important clue of all.Who are the XoneBros?We are your exclusive Xbox Series X & Game Pass weekly podcast. We are more than just a podcast though, we are a positive gaming and Xbox community. We are a group of friends who love gaming, comics, fantasizing about superpowers, and making lame jokes.We strive to bring you news, informative discussion, and rocking good times on a weekly basis all while discussing the world that is Xbox. We are the brothers you never had and the sisters you always wanted... we are the XoneBros. If you are looking for a positive gaming environment, you are always welcome here!⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Support Us On YouTube⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Join our Discord⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠X1TheGamer Daily Xbox News ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠MrMcspicey Know Your Game⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠