Podcasts about gpu

Jordi Visser is a veteran macro investor with 30+ years of experience and the author of the VisserLabs Substack. In this conversation, we discuss rising geopolitical tensions, higher oil prices, and growing risks in private credit and global markets. We also explore bitcoin's resilience, how AI is disrupting software and jobs, and why Jordy believes commodities, liquidity, and volatility will shape the next major investment cycle.====================Need liquidity without selling your crypto? Take out a Figure Crypto-Backed Loan (https://figuremarkets.co/pomp), allowing you to borrow against your BTC, ETH, or SOL with 12-month terms, 8.91% interest rates, and no prepayment penalties. Or check out Democratized Prime (https://figuremarkets.co/pomp) and earn ~8.5% APY on real world assets, paid hourly. Unlock your crypto's potential today at Figure! https://figuremarkets.co/pomp====================Award-winning Fountain Life - Energy supercharged. Memory sharper. Life extended. Ready for the best investment you'll ever make? Schedule a life-changing call at FountainLife.com/Pomp Get $1,000 off the cost of a life-changing membership with Fountain Life when you schedule a call at FountainLife.com/pomp====================This podcast is sponsored by Abra.com. Abra is the secure way to access crypto and crypto based yield and loan products through a separately managed account structure.Learn more at http://www.abra.com.====================Arch Public is an agentic trading platform that automates the buying and selling of your preferred crypto strategies. Sign up today at https://www.archpublic.com and start your automated trading strategy for free. No catch. No hidden fees. Just smarter trading.====================0:00 - Intro0:44 - Iran conflict & oil market shock8:09 - Commodity bull market thesis11:11 - Inflation risks & recession probabilities15:30 - How high could oil go?18:19 - Bitcoin vs gold performance discussion21:12 - Bitcoin as a global “escape hatch” asset29:20 - Inflation vs AI-driven deflation debate33:00 - AI disruption & labor market impact40:48 - Software disruption & tech valuations55:09 - Commodities, compute & future investment themes57:08 - GPU smuggling & AI geopolitics

In this episode of the HVAC School Podcast, host Bryan sits down with Jeff Staub, Director of OEM Sales for Danfoss North America, to explore one of the most rapidly evolving frontiers in the HVAC and refrigeration world: thermal management for AI data centers. With nearly 30 years of industry experience spanning technical support, application engineering, and product development, Jeff brings deep expertise on how the explosive growth of AI chip technology is reshaping data center cooling architecture — and creating major new opportunities for HVAC professionals, contractors, and facility managers alike. A central theme of the conversation is heat recovery — specifically, how the enormous amounts of heat generated by high-density GPU chips in modern data centers can be captured and repurposed rather than simply rejected into the atmosphere. Jeff explains that while heat recovery itself is not a new concept (supermarkets have used reheat coils and heat reclaim for decades), its application in AI data centers presents fresh challenges and possibilities. The heat coming off liquid-cooled server chips typically runs around 90 to 100 degrees Fahrenheit — useful, but not immediately at the temperature needed for most end applications like domestic hot water or space heating. Boosting that heat using heat pumps or feeding it into district energy systems, boiler pre-heat loops, vertical farms, or multifamily housing developments are among the most promising strategies being explored around the world. Jeff highlights a significant contrast between Europe and the United States in how heat recovery is being adopted. In Europe, where district energy networks are widespread, data centers can plug directly into community heating infrastructure — and projections suggest that 80% of European data centers will incorporate heat recovery in the near future. In the US, the picture is more fragmented: while opportunities exist at universities, hospitals, urban mixed-use developments, and facilities co-located with nuclear power plants, the economics are trickier. Key sticking points include who owns the capital expenditure for heat recovery modules and heat pumps, and who ultimately benefits from the recovered heat. Bryan and Jeff discuss how innovative ownership models — with landlords, municipalities, or co-tenants sharing infrastructure — are beginning to unlock these opportunities, and how co-generation arrangements with power stations present exciting long-term potential. The episode wraps up with highly practical guidance for HVAC contractors and facility managers looking to break into the data center space. Jeff encourages technicians not to be intimidated: the fundamentals of vapor compression, chiller systems, and fluid flow that HVAC professionals already know transfer directly to data center work. The key additions are familiarity with large centrifugal and screw compressors, variable frequency drives on pumps, glycol loop management, and central distribution unit (CDU) architectures. Bryan emphasizes that the boundary between HVAC and plumbing will continue to blur as secondary fluid pumping becomes more prevalent — and that staying curious and investing in ongoing training (through manufacturer programs like Danfoss Learning, Carrier University, and others) is the best way to ride this wave rather than get left behind. Both hosts agree: AI data centers are not going away, and the technicians who keep them cool will be indispensable. Topics Covered The evolution of data center cooling — from direct vapor compression on chips, to air-conditioned server rooms (CRAC units), to today's liquid cooling and chiller-loop architectures Why AI GPU chips generate unprecedented heat densities, with individual server racks approaching 250 kW to 1 MW of heat output What heat recovery means in the data center context: capturing hot water (90–100°F) off chip cooling loops instead of rejecting it to outdoor air The concept of 'heat quality' — why low-temperature waste heat is abundant but difficult to use directly, and how heat pumps solve the temperature-lift challenge Real-world heat recovery applications: district energy systems, boiler pre-heat, vertical farms, multifamily housing, hospitals, and universities Europe vs. the US: why district energy adoption makes heat recovery far more common in European data centers, and what the US can learn Business model challenges: who pays for heat recovery infrastructure, and how co-location, municipal incentives, and landlord ownership models can unlock value Co-generation opportunities: feeding recovered heat back into steam turbines at co-located nuclear or power plants How heat recovery makes heat pump technology more viable by raising the source temperature and reducing compression ratio Danfoss's role in data center thermal management — from compressors and drives to plate heat exchangers, CDU flow control, and prepackaged heat recovery modules Refrigerant transitions and what they mean for data center cooling (R-410A to R-454B, CO2 transcritical systems, potential two-phase refrigerant direct-to-chip cooling) The convergence of HVAC and plumbing trades in a world of secondary fluid pumping and isolated refrigerant charges Absorption chiller technology as a potential future use case for low-grade waste heat Advice for contractors: how existing chiller and refrigeration skills translate to data center work, and what new competencies to build Career and training resources: Danfoss Learning, manufacturer universities (Carrier, Trane, McQuay), and leveraging AI tools for self-education The importance of redundancy and uptime in mission-critical data center environments — and what that means for service response expectations   Learn more about Danfoss at danfoss.com/learning Have a question that you want us to answer on the podcast? Submit your questions at https://www.speakpipe.com/hvacschool. Purchase your tickets or learn more about the 7th Annual HVACR Training Symposium at https://hvacrschool.com/symposium. Subscribe to our podcast on your iPhone or Android. Subscribe to our YouTube channel. Check out our handy calculators here or on the HVAC School Mobile App for Apple and Android.

In this episode of Tank Talks, Matt Cohen sits down with Ali Asaria, Co-Founder of Transformer Lab, to unpack the less visible side of the AI boom, from broken machine learning tools to the rise of autonomous research agents. Ali shares what it really looks like inside modern AI development and why the biggest opportunity isn't just using models, but having the ability to train, control, and improve them.Ali also reflects on his journey building across multiple tech waves, from creating BrickBreaker on BlackBerry to scaling Well.ca and Tulip, and now tackling AI infrastructure with Transformer Lab. He breaks down the realities most founders don't talk about, why great products lose deals, how long enterprise sales actually take, and why success often comes down to trust, timing, and people more than technology.Beyond AI, the conversation takes a broader turn into the future of innovation. Ali challenges the tech industry, especially in Canada, to think bigger, rebuild public trust, and focus on solving real-world problems through ambitious “mega projects.” If you're trying to separate AI hype from reality and understand where the real leverage is being created, this episode gives you a much clearer lens.Building BrickBreaker on 150M Devices (00:02:41)How a side project at BlackBerry turned into a global phenomenon. The early lesson that distribution beats perfection. Ali shares how building something simple but widely adopted gave him an early taste of scale. It also shaped his belief that getting into users' hands fast matters more than polishing endlessly in isolation.The Early Days of E-Commerce in Canada (00:05:36)Packing boxes manually, hacking payment systems, and why investors believed e-commerce would never work in Canada. From manually processing credit cards to building infrastructure from scratch, Ali walks through how scrappy the early days really were. It's a reminder that many “obvious” markets today once looked completely unworkable.Scaling Well.ca and the McKesson Exit (00:08:18)How relationships with partners turned into acquisition opportunities. The messy reality behind “successful exits.” Ali explains how long-term partnerships quietly set the stage for acquisition, even before it was intentional. He also highlights how unpredictable and fragile deals can be, even when they seem done.Enterprise Sales Lessons from Tulip (00:11:19)Why great products don't win deals. Trust, relationships, and the human side of multi-million dollar contracts. Ali breaks down how enterprise sales are less about features and more about credibility and relationships built over time. He also shares how incumbents win not because they're better, but because they're already embedded.The Hard Truth About Startup Life (00:13:52)“90% hell, 10% fun.” What founders don't talk about publicly and how to choose the right investors. Behind the highlight reels, Ali emphasizes how difficult the journey really is and how rarely things go to plan. Choosing the right partners becomes critical when things inevitably get hard.The Moment AI Changed Everything (00:16:22)Why language models shattered the belief that human intelligence couldn't be replicated. Ali describes the exact moment his worldview shifted after seeing what LLMs could do. What once felt impossible suddenly became inevitable, changing how he thought about both technology and opportunity.What Transformer Lab Actually Does (00:20:11)Simplifying AI model training, orchestration, and infrastructure across local machines and massive GPU clusters. Ali explains how fragmented and complex current AI workflows are, especially for researchers. Transformer Lab aims to remove that friction and make building models far more accessible and efficient.Scaling AI From One Machine to Thousands (00:23:14)The technical leap required to move from hobbyist experimentation to full-scale AI labs. Moving from a single machine to distributed systems introduces massive complexity most developers never see. Ali breaks down why solving this unlock is essential for the next generation of AI builders.AI Hype vs Reality (00:25:41)Why Ali believes we may already have AGI, and why valuations still don't make sense. Ali challenges the common narrative by arguing we're closer to AGI than people admit. At the same time, he questions whether the current market can realistically justify the valuations we're seeing.Canada's Startup Ecosystem: Challenges & Advantages (00:32:11)Why geography matters less than mindset, and why building is always hard everywhere. Ali pushes back on the idea that location is the primary constraint for founders. Instead, he argues that resilience and ambition matter far more than where you're building from.Why Tech Has Lost Public Trust (00:34:12)From rebels to power players, and what founders must do to rebuild credibility. Ali reflects on how the tech industry's image has shifted over time and why that matters. Rebuilding trust requires focusing on real impact, not just growth or financial wins.The Case for Mega Projects (00:38:09)Why Canada needs bold, visible innovation bets that actually improve everyday life. Ali argues that large-scale, collaborative initiatives could realign public perception and drive meaningful progress. The key is solving problems people actually feel in their daily lives.The Future of AI and Talent Sovereignty (00:41:28)Why owning talent matters more than owning infrastructure in the AI race. Ali emphasizes that long-term advantage comes from people, not just technology or compute. Countries that develop and retain top talent will ultimately shape the future of AI.About Ali AsariaAli Asaria is a serial entrepreneur and one of Canada's most accomplished technology founders. He created the iconic BrickBreaker game on BlackBerry, founded Well.ca (later acquired by McKesson), and built Tulip into a leading enterprise retail platform backed by top-tier investors.He is now the co-founder of Transformer Lab, an open-source platform designed to simplify and scale AI model development. His work focuses on democratizing access to AI infrastructure, enabling developers and organizations to build advanced models without the complexity traditionally required.Ali is known for his bold thinking on AI, startup ecosystems, and the future of technology, often challenging conventional narratives around innovation and scale.Connect with Ali Asaria on LinkedIn: https://www.linkedin.com/in/aliasaria/Visit the Transformer Lab website: https://lab.cloud/Connect with Matt Cohen on LinkedIn: https://ca.linkedin.com/in/matt-cohen1Visit the Ripple Ventures website: https://www.rippleventures.com/ This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit tanktalks.substack.com

For episode 694 of the BlockHash Podcast, host Brandon Zemp is joined by Trevor Harries-Jones, Board Director for Render Network, a decentralized GPU network powering some of the world's biggest visual and entertainment projects, including Las Vegas Sphere visuals, Super Bowl trailers, and Coachella stage shows. They are emerging as a counterweight to GPU consolidation and an alternative compute layer for AI and real-time rendering. 

A series of “extraordinary” events has made the environment more challenging for growth stocks. But “this level of trepidation can't go on forever”, says Baillie Gifford partner Stuart Dunbar in this latest episode, suggesting that patient investors will benefit when stability returns and the markets value exceptional companies at a premium again. Stuart Dunbar is a director in Baillie Gifford's Clients Department and is responsible for helping shape and communicate the firm's investment philosophy.In this conversation, he considers how a succession of disruptive events – the most recent being the current war in the Middle East – has rattled markets and led investors to focus on companies' short-term profits rather than their long-term potential.However, this period of flux will not last forever, he argues. And when we re-enter a period of stability, patience should be rewarded as markets recognise exceptional companies' future earnings potential and price them accordingly. In the meantime, Baillie Gifford's investment teams remain focused on finding and supporting businesses that will prosper from change and supporting their management to take the long view. And as Dunbar reveals, as the sources of growth broaden out, we are backing some companies that come as a surprise.  Portfolio companies discussed include:Astera Labs – the semiconductor chip designer, whose products tackle data bottlenecks in AI datacentresIREN – the datacentre operator whose clients include MicrosoftMedpace – a contract research organisation that biotech and pharmaceutical companies hire to run their clinical trialsNu Holdings – owner of the Latin American fintech NubankSpotify – the audio streaming platform that lets people listen to music, podcasts and audiobooksWillScot – North America's largest provider of temporary space rentals, leasing out modular offices, portable storage containers and classroom units  Resources:Actual investors hubActual investing revisitedBaillie Gifford podcastsPrivate growth investingThe Compound and Friends podcastThe Success Equation Companies mentioned include:AJ BellAmazonAnthropicAstera LabsByteDanceIRENMedpaceMicrosoftNu HoldingsNVIDIASpotifyWillScot Timecodes:00:00  Introduction02:00  Active v passive03:35  “Know what we own”06:15  Building relationships with company leaders07:55  Causes and effects of uncertainty11:05  Beyond the Magnificent 712:45  A period of relative stability17:50  Compressed valuations19:25  Nubank and Medpace's promise23:10  Meetings with clients25:40  Broader sources of growth28:15  Private equity growth31:25  Better-informed stock picking33:25  Staying independent and standalone35:45  “Wait until the market comes to its senses”37:10  Book choiceGlossary of terms (in order of mention):  Latent heat: energy absorbed or released during a change of state, like ice melting, without a change in temperature.Active investing: trying to beat the market by choosing investments based on research and judgement.Passive funds: investment funds that track a market index rather than picking stocks actively.Quantitative approaches: investment methods that use data, models and statistics to make decisions.Market capitalisation weights: an index method that gives bigger companies a larger influence based on their total market value.Alignment of incentives: making sure different parties are rewarded in ways that encourage the same goals.Drawdowns: significant falls in the value of an investment from a previous peak.R&D: research and development – spending on innovation and new products or technologies.Backdate options: setting share-option dates retrospectively to make them more valuable, often controversially.Shareholder registers: the official records of who owns a company's shares.Benchmark: a standard, often an index, used to compare investment performance.Magnificent 7 / Mag 7: the seven giant US tech stocks that have dominated market performance in recent years.GPU: graphics processing unit – a specialised chip often used for AI computing because it handles parallel tasks well.Sub-market multiple: a valuation lower than the market average.Strategic asset allocation: deciding how much to invest in broad asset classes like shares, bonds or private markets.Benchmark-aware: closely focused on performance relative to a benchmark index.Venture capital: investment in early-stage, high-growth private companies.Private equity buyout funds: funds that buy controlling stakes in companies, often using debt.Private equity growth: investing in more mature private companies that are expanding but not yet public.Roadshow: presentations by company leaders to investors ahead of an IPO or fundraising.Alternative asset classes: investments outside traditional shares and bonds, such as private equity or infrastructure.Path dependency: the idea that outcomes are shaped by the sequence of earlier decisions and events.  

This year's MWC took place as the telecom industry is at a crossroads, with additional monetization of 5G beyond mobile broadband less certain, smartphone growth flattening, AI influence increasing, and more questions than answers about the future. In this episode, Neil Shah of Counterpoint Research, Leonard Lee of Next Curve, and I discuss our experience at the event, and analyze the traction and monetization of 5G Advanced, Autonomous Networks, compare and contrast the progress of Western and Asian markets, the opportunity for AI for telecom, early use cases, and the prospect of RAN for AI. 6G and more. We also delve into whether telcos are better positioned for the sovereign AI and Data Center market. Index: 00:00 - Intro 00:35 - Guest intro (Neil Shah, Leonard Lee) 01:10 - MWC attendance 02:23 - Major themes of the event - 5G Advanced, AI, Autonomous Networks, 6G 07:00 - AI Ops for operators (AI for Telco) - Customer Care, Marketing, Billing, HR, Network Management, etc. 14:43 - AI for Networks Ops - Challenges,(data for AI) opportunities, and progress so far 17:38 - Autonomous Networks - Chinese operators at Level-4 (pockets), others at Level-2/2.5 21:30 - Current status of AI - Frank talk by Samsung Network executives on the current status 23:14 - Challenges of extending Autonomous Networks beyond China (by Chinese vendors), need for monetization opportunities 25:56 - Can the 5G Advanced monetization use case, successful in China, work in the US/Europe? 29:42 - RAN for AI, feasibility of GPU at Base stations, and challenges (power, weight, space) 33:29 - 6G - Qualcomm sensing demos, Ericsson/Apple - 5G/6G spectrum sharing (MRS) demo, uplink, need for monetization going beyond wireless service 41:25 - Are operators better positioned to offer Sovereign AI Data Centers - Deutsche Telekom's strategy, similar approach by Middle East /Korea, Is sovereignty is about data or also includes models?  50:50 - Did MWC 2026 move the needle for operators? 52:35 - Closing

You'll have to forgive me for getting completely sidetracked as we turn to someone who bought a pre-built PC. But not just ANY pre-built. Dude went ALL out with 64 gigs of RAM and a massive 5090 GPU. So, yeah, that kinda grabbed my attention a bit there. Given the times we currently live in when we talk about RAM prices going way up and causing the prices of graphics cards to also increase. All of this was after we started talking about what the next Xbox would likely cost. And what Valve's Steam Machine might also cost. New tech is always exciting but no matter the thrills, the specs, the bells AND the whistles…it'll all get kind of buried under the weight of whatever the price tag is. And that's currently unknown for us. And may be for some time. At any other point in time previous, we'd already know the price. We'd already have the details. But they're taking their time on getting there and it's because of the price of RAM. Full stop. The Switch 2 got in under the gun and, for now, was able to avoid the spike in prices. But that won't last much longer before they have to replenish stock and the price will likely affect that. But Valve’s new PC will be our first look at just how bad it has become. And give us a better idea of what to expect from the next Xbox and the PS6. Start saving. Ubisoft, it's been 4,591 days since a new Splinter Cell game (non-animated series or guest spot in another game franchise, remake, BBC radio drama, or VR exclusive) was released. Also, there's been 1,626 job losses in the gaming industry since January 1, 2026.

In this episode of Tank Talks, Matt Cohen and John Ruffolo unpack a volatile moment across software, capital markets, AI, and Canadian industrial policy. The conversation opens with Constellation Software's AI-era challenge, as new president Mark Miller faces investor skepticism around whether legacy vertical market software can maintain its moat in a world increasingly shaped by AI-driven productivity, automation, and code generation.From there, Matt and John examine Salesforce's decision to raise billions in debt to fund share buybacks, questioning whether this is smart balance-sheet engineering or a red flag that large software companies are running out of offensive growth options. The episode then turns to the private credit market, where redemption gates, liquidity pressure, and fears around AI infrastructure lending raise deeper concerns about leverage, accounting, and systemic fragility.Back in Canada, the discussion shifts to the country's defence industrial strategy and why the real opportunity is not just traditional military spending, but dual-use investment across AI, quantum, satellites, aerospace, and strategic infrastructure. The episode closes with a look at Andrej Karpathy's open-source Auto Research project and what it signals about the speed of AI progress, the democratization of research capabilities, and the growing pressure on knowledge workers and software engineers to keep up.If software moats are weakening, private credit is wobbling, and defence dollars are becoming innovation dollars, where will the next real edge come from?Constellation Software, AI Pressure, and the Future of Vertical SaaS (00:43)Matt and John break down Constellation Software's latest numbers, the market's growing skepticism toward legacy software businesses, and the bigger question of whether mission-critical vertical SaaS can stay resilient as AI chips away at traditional moats. They explore why trusted workflows and proprietary data still matter, but also why even durable software businesses may face long-term pressure.Salesforce's $25 Billion Debt Bet and What It Really Signals (06:28)Matt and John unpack Salesforce's plan to raise massive debt for share buybacks, debating whether this is efficient capital structure management or a defensive move from a software giant with fewer compelling growth opportunities. The bigger issue is what this says about confidence, capital allocation, and the mood inside mature SaaS companies right now.Private Credit Redemption Gates and the Fear Beneath the Surface (10:49)A wave of redemption limits across major private credit funds becomes the next flashpoint. Matt and John explain why retail money flooded into the asset class, how managers were pushed into riskier lending, and why the underlying concern is no longer just liquidity management, but whether private credit has been pricing equity risk like it was safe debt.Canada's Defense Strategy Is Really a Dual-Use Tech Strategy (16:29)Matt and John shift to Canada's defense industrial strategy and the National Research Council's planned investment, arguing that the real opportunity is in dual-use innovation. Rather than thinking only in terms of tanks and submarines, John reframes defense spending as investment in AI, quantum, satellites, aerospace, and strategic infrastructure that can serve both government and enterprise customers.The AI Catch-Up Panic Is Real (21:26)Matt and John zoom out from markets and policy to the personal reality of AI acceleration. John admits he feels both energized and behind, capturing the exact tension many operators and investors feel as new tools emerge faster than most people can realistically absorb them.Andrej Karpathy Auto Research and the One-GPU Research Lab Moment (22:58)The episode closes with Andrej Karpathy's open-source Auto Research project and why it matters. Matt explains how autonomous research loops, overnight experimentation, and low-cost GPU access could dramatically speed up model tuning, product testing, and AI development, making advanced experimentation far more accessible than before.Connect with John Ruffolo on LinkedIn: https://ca.linkedin.com/in/joruffoloConnect with Matt Cohen on LinkedIn: https://ca.linkedin.com/in/matt-cohen1Visit the Ripple Ventures website: https://www.rippleventures.com/ This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit tanktalks.substack.com

Crypto OG Erik Voorhees joins The Chopping Block crew to dissect the future of agentic payments, the eternal war for privacy, memecoin-fueled AI drama on Moltbook, and why your next DeFi user might just be your OpenClaw agent—plus, a candid look at crypto's core and how AI turns software engineering existential. Welcome to The Chopping Block — where crypto insiders Haseeb Qureshi, Tom Schmidt, Tarun Chitra, and Robert Leshner chop it up about the latest in crypto. This week, we're joined by none other than Erik Voorhees, legendary crypto pioneer and founder of Venice, for a no-holds-barred discussion on the wild convergence of AI, crypto, and the meme coin casino. Erik unpacks his journey from anti-surveillance crusader to AI entrepreneur, why Venice is all-in on privacy and free speech for LLMs, and how “provable privacy” is a Sisyphean technical challenge. The crew breaks down OpenClaw's agent drama, memecoin carpet-bombing of Moltbook, and Meta muscling in on AI social networks. We debate agentic payments (will your first paying customer soon be a bot?), the true game theory behind state surveillance, and why crypto's greatest killer use case might actually be building tools for robots instead of humans. Plus: existential crises for software engineers, why “AI alignment” is a philosophical dead end, and the childlike glee (or open psychosis) of trading OpenClaw war stories at AI meetups. Listen to the episode on Apple Podcasts, Spotify, Pods, Fountain, Podcast Addict, Pocket Casts, Amazon Music, or on your favorite podcast platform. Show highlights

What does it take to design a data center for a world where the technology inside it may change several times before the building even opens? In this episode of Tech Talks Daily, I sit down with Jackson Metcalf, Principal at Gensler, to talk about how AI is forcing a complete rethink of data center design. Jackson has spent nearly two decades working on critical facilities, and in our conversation he explains how the shift from traditional cloud workloads to dense AI environments is changing everything from building form and cooling strategy to long-term infrastructure planning. What struck me most in this conversation is the sheer mismatch in timescales. Data centers can take two and a half to three years to design and build, while chip and GPU roadmaps are evolving in cycles of months. Jackson explains why that means designing for a fixed end state no longer makes sense. Instead, the future may belong to facilities built with flexibility at their core, spaces that can be reconfigured, upgraded, and even conceptually rebuilt over time rather than treated as static assets. We also talk about what hyper-flexibility actually means in practice. This is not just a buzzword. It is about designing buildings with enough structural and engineering headroom to support very different cooling and power models over their lifespan. As AI workloads push cabinet densities to levels that would have sounded impossible only a few years ago, the need for plug-and-play mechanical and electrical infrastructure becomes far more than a design preference. It becomes essential. Another fascinating part of the conversation centers on sustainability. Jackson shares why durable, well-built structures can create long-term environmental value, even in an industry often criticized for its energy demands. We discuss embodied carbon, adaptive reuse, and why a high-quality building may have a much better second life than something built purely for short-term speed. That leads into a wider conversation about repositioning underused real estate, from former industrial facilities to vacant office buildings, as potential digital infrastructure. We also get into the growing energy challenge behind AI. With demand for power rising fast, and the US grid under increasing pressure, many operators are now weighing options such as on-site natural gas generation while waiting for cleaner long-term alternatives to mature. Jackson offers a thoughtful perspective on the tension between urgent infrastructure needs and environmental responsibility, as well as the uncertainty surrounding future energy roadmaps. Looking further ahead, I ask Jackson what will define a successful data center campus in the years to come. Will it be raw megawatts, adaptability, carbon intensity, location strategy, or something else entirely? His answer opens up a much bigger conversation about whether these buildings can become more connected to the communities around them, and what role they may play in a future where digital infrastructure is no longer hidden in the background, but central to how society functions. So if AI is pushing data center design to extremes, how do we build facilities that are ready for what comes next without becoming obsolete almost as soon as they open? And what does sustainable, adaptable digital infrastructure really look like in practice?

I break down Andrej Karpathy's new open-source project, Autoresearch: what it is, how it works, and why some of the smartest people in tech are losing their minds over it. I walk through 10 concrete business ideas you can build on top of Autoresearch loops, from niche agent-in-a-box products to always-on A/B testing agencies. I also cover Karpathy's companion launch, Agent Hub, share community reactions, and show you step by step how to get started using Claude Code and a Colab GPU. I'm hosting a free workshop so you can build your business in the age of AI. Sign up here: https://startup-ideas-pod.link/build-with-ai-2026 Links Mentioned: Autoresearch Github: https://startup-ideas-pod.link/autoresearch Timestamps 00:00 – Intro 00:45 – How Autoresearch Actually Works 02:40 – Visual Walkthrough of the Autoresearch Loop 03:37 – Mental Model: Your Research Bot That Runs While You Sleep 05:26 – Idea 1: Niche Agent-in-a-Box Products 06:48 – Idea 2: A/B Testing for Marketing (Landing Pages & Ads) 08:45 – Idea 3: Research as a Service 09:43 – Idea 4: Power Tool Inside Your Own SaaS 10:49 – Idea 5: Agency That Runs 100× More Tests 12:05 – Idea 6: Auto Quant for Trading Ideas 13:44 – Idea 7: Always-On Lead Qualification & Follow-Up 14:21 – Idea 8: Finance Ops Autopilot for Businesses 15:09 – Idea 9: Internal Productivity Lab for Your Org 15:53 – Idea 10: Done-for-You Research & Due Diligence Shop 16:41 – Non business use cases 18:27 – Karpathy's Agent Hub Announcement 19:50 – How to Get Started with Autoresearch 22:21 – Final Thoughts Key Points Autoresearch is an open-source AI agent that sets a goal, runs experiments in a loop on a GPU, keeps the winners, and discards the rest — all while you sleep. You need an NVIDIA GPU to run it (tested on H100), but you can rent one cheaply through Lambda Labs, Vast AI, RunPod, Google Cloud, or Google Colab. The fastest way to get started is to use Claude Code to walk you through installation, then run it on Google Colab with a T4 GPU runtime. Ten business ideas built on Autoresearch span niches like SaaS optimization, A/B testing agencies, trading backtests, CRM lead scoring, and done-for-you due diligence. Karpathy also launched Agent Hub — essentially a GitHub designed for agent swarms to collaborate on the same codebase. The project already has 25,000+ GitHub stars and is growing fast; early movers who tinker now build an unfair advantage. The #1 tool to find startup ideas/trends - https://www.ideabrowser.com LCA helps Fortune 500s and fast-growing startups build their future - from Warner Music to Fortnite to Dropbox. We turn 'what if' into reality with AI, apps, and next-gen products https://latecheckout.agency/ The Vibe Marketer - Resources for people into vibe marketing/marketing with AI: https://www.thevibemarketer.com/ FIND ME ON SOCIAL X/Twitter: https://twitter.com/gregisenberg Instagram: https://instagram.com/gregisenberg/ LinkedIn: https://www.linkedin.com/in/gisenberg/

Send a textReady for a reality check on AI security? We invited Cisco cybersecurity expert Katherine McNamara to dig into where large language models actually break: from prompt injection and over-permissioned plugins to reckless “vibe-coded” apps that leak IDs, photos, and entire backends. The stories are real, the stakes are high, and the fixes are concrete. We trace how AI sprawl mirrors the worst of early IoT—weak defaults, poor isolation, and a stampede to integrate models into billing, HR, and support without guardrails—only this time the blast radius includes your customer data and your legal exposure.We talk through the human factor first. Written policies won't stop someone from pasting a pen test report into a public chatbot. DLP helps, but hybrid work and BYOD stretch defenses thin. Then we move to the core threat model: public and private models are targets; datasets can be poisoned; plugins often ship with admin-level scopes; and a clever prompt can trick an LLM into disclosing chat histories, creating new accounts, or modifying orders. Courts have already treated chatbots as company representatives, binding businesses to their outputs—another reason to treat every integration like an untrusted user with strict least privilege.It's not all doom. Used well, AI gives security operations superpowers: correlating signals across dozens of tools, reducing alert fatigue, and surfacing lateral movement. The path forward is discipline, not denial. Fence models on the network. Prefer read-only to write. Gate plugins behind narrowly scoped APIs. Vet datasets for backdoors. Red-team prompts as seriously as you pen test code. And educate stakeholders with live demos so they see why these controls matter. We also unpack the shaky economics—GPU costs, rising consumer fatigue, hype-fueled projects with little ROI—and why that pressure can erode privacy if teams aren't vigilant.If you're building with LLMs or trying to rein them in, this conversation gives you a practical map: what to allow, what to block, and how to make AI useful without turning your stack into an attack surface. Subscribe, share with a teammate who ships integrations, and drop a review with the one guardrail you'll implement this quarter.Connect with our Guest:https://x.com/kmcnam1https://www.linkedin.com/in/katherinermcnamara/Purchase Chris and Tim's book on AWS Cloud Networking: https://www.amazon.com/Certified-Advanced-Networking-Certification-certification/dp/1835080839/ Check out the Monthly Cloud Networking Newshttps://docs.google.com/document/d/1fkBWCGwXDUX9OfZ9_MvSVup8tJJzJeqrauaE6VPT2b0/Visit our website and subscribe: https://www.cables2clouds.com/Follow us on BlueSky: https://bsky.app/profile/cables2clouds.comFollow us on YouTube: https://www.youtube.com/@cables2clouds/Follow us on TikTok: https://www.tiktok.com/@cables2cloudsMerch Store: https://store.cables2clouds.com/Join the Discord Study group: https://artofneteng.com/iaatj

Send a textPrices climb, hype fades, and the “console war” story we grew up with starts to feel like a rerun. We dig into why the battlefield is shifting from living rooms to ecosystems, and how a rumored Microsoft move—Project Helix—could reshape Xbox into something closer to a prebuilt PC with AI at its core. That sounds powerful, but not if the sticker reads four figures while Steam's handhelds and machines ship at a friendlier price and Sony keeps its place with focused hardware and polished franchises.We connect the dots from everyday tech frustration—the Nothing 4A skipping the U.S., Tubi feeling like the new UPN with too many ads—to the larger pattern: companies chasing margins, users chasing value. On the gaming side, Nvidia remains the engine under everything AI, which means RAM and GPU costs stay high and “consoles” start looking like workstations. If that's where Xbox heads, we see a future where Microsoft leans hard into software and services—Game Pass, cloud streaming, publishing across platforms—while hardware slowly steps off stage. Meanwhile, Steam's momentum and the rise of Linux-based gaming make PC-level flexibility feel easy enough for more players to try.We also talk practicality: parents won't buy $1,000 boxes, and many players are better off building a PC over time, owning their library, and keeping options open. Expect AI to become a built-in coach and tuner for players and a force-multiplier for developers. Expect more crossovers between handheld PCs, TVs, and laptops. And expect the winners to be the platforms that respect budgets, reduce friction, and make great games simple to play anywhere.If you care about where to put your money next—console, PC, or cloud—this breakdown helps you map the trade-offs and spot the real value. Subscribe, share with a friend who's weighing an upgrade, and drop your take: are you building a PC, sticking with a console, or going handheld next? https://www.carolinaotakus.com/

Markets surge, war spreads, California buys, and New York opens the door.Thomas Hunt ( https://www.twitter.com/madbitcoins)THIS WEEK: Bitcoin Surges to $74,000 After President Trump Throws Support Behind Key Crypto Billhttps://www.investopedia.com/bitcoin-surges-after-president-trump-throws-support-behind-key-crypto-bill-11919161Bitcoin (BTC) is quickly giving up its weekly gains — here's whyhttps://www.coindesk.com/markets/2026/03/06/no-deal-with-iran-trump-demands-unconditional-surrender-sending-oil-surging-bitcoin-and-stocks-lowerThe Secret Bitcoin Loophole Helping Iran Fire Missiles At Israel And US Without Going Brokehttps://www.ndtv.com/world-news/the-secret-bitcoin-loophole-helping-iran-fire-missiles-at-israel-and-us-without-going-broke-11175328California's taxpayer-backed pension systems invest in Bitcoin and crypto – Orange County Registerhttps://www.ocregister.com/2026/03/06/californias-taxpayer-backed-pension-systems-invest-in-bitcoin-and-crypto/Strike Secures New York BitLicense, Opening Bitcoin Financial Services To State Residentshttps://bitcoinmagazine.com/news/strike-secures-new-york-bitlicenseTim Draper On The AI Boom, Bitcoin's Future And Building ‘Human Accelerators'https://news.crunchbase.com/venture/tim-draper-ai-bitcoin-human-accelerators/Food Lion manager stops elderly woman from losing $5K in bitcoin scamhttps://www.wistv.com/2026/03/06/food-lion-manager-stops-elderly-woman-losing-5k-bitcoin-scam/Bitcoin News on X: "SOUTH KOREA TAX OFFICE LEAKS CRYPTO SEED PHRASE, $4.8M IN TOKENS DRAINED South Korea's National Tax Service accidentally exposed a crypto wallet seed phrase in an official press release tied to a tax enforcement campaign, leading to the loss of 4 million PRTG tokens worth about https://t.co/blyoGS8Cpg" / Twitterhttps://x.com/bitcoinnewscom/status/2027461233457467653Sweep on X: "Everyone knows about the 10,000 Bitcoin pizza but nobody talks about the other 150,000 BTC he wasted after Before the famous pizza order, Laszlo Hanyecz invented GPU mining for Bitcoin Which made it possible for him to mine thousands of BTC per day Satoshi personally messaged https://t.co/DNw0BIIuUc" / Twitterhttps://x.com/0xsweep/status/2027431656890454457Bitcoin's hard fork proposal to get back $5 billion in stolen Mt. Gox funds sees no takershttps://www.coindesk.com/tech/2026/02/28/former-mt-gox-ceo-proposed-a-rewrite-of-bitcoin-s-code-to-recover-usd5-billion-in-stolen-funds-gets-quickly-shutdownDeveloper embeds image on Bitcoin as a single transaction, challenging BIP-110's core claims | The Blockhttps://www.theblock.co/post/391667/developer-embeds-image-on-bitcoin-as-a-single-transaction-challenging-bip-110s-core-claimsbeeple on X: "WORLD WAR MEME https://t.co/2O6QrwUSeo" / Twitterhttps://x.com/beeple/status/2028333614694322220__________________________________________________________________________________World Crypto Network https://www.worldcryptonetwork.com/On This Day in World Crypto Network Historyhttps://www.worldcryptonetwork.com/onthisday/---------------------------------------------------------------------------Please Subscribe to our Youtube Channelhttps://m.youtube.com/channel/UCR9gdpWisRwnk_k23GsHf

Join Kyle, Nader, Vibhu, and swyx live at NVIDIA GTC next week!Now that AIE Europe tix are ~sold out, our attention turns to Miami and World's Fair!The definitive AI Accelerator chip company has more than 10xed this AI Summer:And is now a $4.4 trillion megacorp… that is somehow still moving like a startup. We are blessed to have a unique relationship with our first ever NVIDIA guests: Kyle Kranen who gave a great inference keynote at the first World's Fair and is one of the leading architects of NVIDIA Dynamo (a Datacenter scale inference framework supporting SGLang, TRT-LLM, vLLM), and Nader Khalil, a friend of swyx from our days in Celo in The Arena, who has been drawing developers at GTC since before they were even a glimmer in the eye of NVIDIA:Nader discusses how NVIDIA Brev has drastically reduced the barriers to entry for developers to get a top of the line GPU up and running, and Kyle explains NVIDIA Dynamo as a data center scale inference engine that optimizes serving by scaling out, leveraging techniques like prefill/decode disaggregation, scheduling, and Kubernetes-based orchestration, framed around cost, latency, and quality tradeoffs. We also dive into Jensen's “SOL” (Speed of Light) first-principles urgency concept, long-context limits and model/hardware co-design, internal model APIs (https://build.nvidia.com), and upcoming Dynamo and agent sessions at GTC.Full Video pod on YouTubeTimestamps00:00 Agent Security Basics00:39 Podcast Welcome and Guests07:19 Acquisition and DevEx Shift13:48 SOL Culture and Dynamo Setup27:38 Why Scale Out Wins29:02 Scale Up Limits Explained30:24 From Laptop to Multi Node33:07 Cost Quality Latency Tradeoffs38:42 Disaggregation Prefill vs Decode41:05 Kubernetes Scaling with Grove43:20 Context Length and Co Design57:34 Security Meets Agents58:01 Agent Permissions Model59:10 Build Nvidia Inference Gateway01:01:52 Hackathons And Autonomy Dreams01:10:26 Local GPUs And Scaling Inference01:15:31 Long Running Agents And SF ReflectionsTranscriptAgent Security BasicsNader: Agents can do three things. They can access your files, they can access the internet, and then now they can write custom code and execute it. You literally only let an agent do two of those three things. If you can access your files and you can write custom code, you don't want internet access because that's one to see full vulnerability, right?If you have access to internet and your file system, you should know the full scope of what that agent's capable of doing. Otherwise, now we can get injected or something that can happen. And so that's a lot of what we've been thinking about is like, you know, how do we both enable this because it's clearly the future.But then also, you know, what, what are these enforcement points that we can start to like protect?swyx: All right.Podcast Welcome and Guestsswyx: Welcome to the Lean Space podcast in the Chromo studio. Welcome to all the guests here. Uh, we are back with our guest host Viu. Welcome. Good to have you back. And our friends, uh, Netter and Kyle from Nvidia. Welcome.Kyle: Yeah, thanks for having us.swyx: Yeah, thank you. Actually, I don't even know your titles.Uh, I know you're like architect something of Dynamo.Kyle: Yeah. I, I'm one of the engineering leaders [00:01:00] and a architects of Dynamo.swyx: And you're director of something and developers, developer tech.Nader: Yeah.swyx: You're the developers, developers, developers guy at nvidia,Nader: open source agent marketing, brev,swyx: and likeNader: Devrel tools and stuff.swyx: Yeah. BeenNader: the focus.swyx: And we're, we're kind of recording this ahead of Nvidia, GTC, which is coming to town, uh, again, uh, or taking over town, uh, which, uh, which we'll all be at. Um, and we'll talk a little bit about your sessions and stuff. Yeah.Nader: We're super excited for it.GTC Booth Stunt Storiesswyx: One of my favorite memories for Nader, like you always do like marketing stunts and like while you were at Rev, you like had this surfboard that you like, went down to GTC with and like, NA Nvidia apparently, like did so much that they bought you.Like what, what was that like? What was that?Nader: Yeah. Yeah, we, we, um. Our logo was a chaka. We, we, uh, we were always just kind of like trying to keep true to who we were. I think, you know, some stuff, startups, you're like trying to pretend that you're a bigger, more mature company than you are. And it was actually Evan Conrad from SF Compute who was just like, you guys are like previousswyx: guest.Yeah.Nader: Amazing. Oh, really? Amazing. Yeah. He was just like, guys, you're two dudes in the room. Why are you [00:02:00] pretending that you're not? Uh, and so then we were like, okay, let's make the logo a shaka. We brought surfboards to our booth to GTC and the energy was great. Yeah. Some palm trees too. They,Kyle: they actually poked out over like the, the walls so you could, you could see the bread booth.Oh, that's so funny. AndNader: no one else,Kyle: just from very far away.Nader: Oh, so you remember it backKyle: then? Yeah I remember it pre-acquisition. I was like, oh, those guys look cool,Nader: dude. That makes sense. ‘cause uh, we, so we signed up really last minute, and so we had the last booth. It was all the way in the corner. And so I was, I was worried that no one was gonna come.So that's why we had like the palm trees. We really came in with the surfboards. We even had one of our investors bring her dog and then she was just like walking the dog around to try to like, bring energy towards our booth. Yeah.swyx: Steph.Kyle: Yeah. Yeah, she's the best,swyx: you know, as a conference organizer, I love that.Right? Like, it's like everyone who sponsors a conference comes, does their booth. They're like, we are changing the future of ai or something, some generic b******t and like, no, like actually try to stand out, make it fun, right? And people still remember it after three years.Nader: Yeah. Yeah. You know what's so funny?I'll, I'll send, I'll give you this clip if you wanna, if you wanna add it [00:03:00] in, but, uh, my wife was at the time fiance, she was in medical school and she came to help us. ‘cause it was like a big moment for us. And so we, we bought this cricket, it's like a vinyl, like a vinyl, uh, printer. ‘cause like, how else are we gonna label the surfboard?So, we got a surfboard, luckily was able to purchase that on the company card. We got a cricket and it was just like fine tuning for enterprises or something like that, that we put on the. On the surfboard and it's 1:00 AM the day before we go to GTC. She's helping me put these like vinyl stickers on.And she goes, you son of, she's like, if you pull this off, you son of a b***h. And so, uh, right. Pretty much after the acquisition, I stitched that with the mag music acquisition. I sent it to our family group chat. Ohswyx: Yeah. No, well, she, she made a good choice there. Was that like basically the origin story for Launchable is that we, it was, and maybe we should explain what Brev is andNader: Yeah.Yeah. Uh, I mean, brev is just, it's a developer tool that makes it really easy to get a GPU. So we connect a bunch of different GPU sources. So the basics of it is like, how quickly can we SSH you into a G, into a GPU and whenever we would talk to users, they wanted A GPU. They wanted an A 100. And if you go to like any cloud [00:04:00] provisioning page, usually it's like three pages of forms or in the forms somewhere there's a dropdown.And in the dropdown there's some weird code that you know to translate to an A 100. And I remember just thinking like. Every time someone says they want an A 100, like the piece of text that they're telling me that they want is like, stuffed away in the corner. Yeah. And so we were like, what if the biggest piece of text was what the user's asking for?And so when you go to Brev, it's just big GPU chips with the type that you want withswyx: beautiful animations that you worked on pre, like pre you can, like, now you can just prompt it. But back in the day. Yeah. Yeah. Those were handcraft, handcrafted artisanal code.Nader: Yeah. I was actually really proud of that because, uh, it was an, i I made it in Figma.Yeah. And then I found, I was like really struggling to figure out how to turn it from like Figma to react. So what it actually is, is just an SVG and I, I have all the styles and so when you change the chip, whether it's like active or not it changes the SVG code and that somehow like renders like, looks like it's animating, but it, we just had the transition slow, but it's just like the, a JavaScript function to change the like underlying SVG.Yeah. And that was how I ended up like figuring out how to move it from from Figma. But yeah, that's Art Artisan. [00:05:00]Kyle: Speaking of marketing stunts though, he actually used those SVGs. Or kind of use those SVGs to make these cards.Nader: Oh yeah. LikeKyle: a GPU gift card Yes. That he handed out everywhere. That was actually my first impression of thatNader: one.Yeah,swyx: yeah, yeah.Nader: Yeah.swyx: I think I still have one of them.Nader: They look great.Kyle: Yeah.Nader: I have a ton of them still actually in our garage, which just, they don't have labels. We should honestly like bring, bring them back. But, um, I found this old printing press here, actually just around the corner on Ven ness. And it's a third generation San Francisco shop.And so I come in an excited startup founder trying to like, and they just have this crazy old machinery and I'm in awe. ‘cause the the whole building is so physical. Like you're seeing these machines, they have like pedals to like move these saws and whatever. I don't know what this machinery is, but I saw all three generations.Like there's like the grandpa, the father and the son, and the son was like, around my age. Well,swyx: it's like a holy, holy trinity.Nader: It's funny because we, so I just took the same SVG and we just like printed it and it's foil printing, so they make a a, a mold. That's like an inverse of like the A 100 and then they put the foil on it [00:06:00] and then they press it into the paper.And I remember once we got them, he was like, Hey, don't forget about us. You know, I guess like early Apple and Cisco's first business cards were all made there. And so he was like, yeah, we, we get like the startup businesses but then as they mature, they kind of go somewhere else. And so I actually, I think we were talking with marketing about like using them for some, we should go back and make some cards.swyx: Yeah, yeah, yeah. You know, I remember, you know, as a very, very small breadth investor, I was like, why are we spending time like, doing these like stunts for GPUs? Like, you know, I think like as a, you know, typical like cloud hard hardware person, you go into an AWS you pick like T five X xl, whatever, and it's just like from a list and you look at the specs like, why animate this GP?And, and I, I do think like it just shows the level of care that goes throughout birth and Yeah. And now, and also the, and,Nader: and Nvidia. I think that's what the, the thing that struck me most when we first came in was like the amount of passion that everyone has. Like, I think, um, you know, you talk to, you talk to Kyle, you talk to, like, every VP that I've met at Nvidia goes so close to the metal.Like, I remember it was almost a year ago, and like my VP asked me, he's like, Hey, [00:07:00] what's cursor? And like, are you using it? And if so, why? Surprised at this, and he downloaded Cursor and he was asking me to help him like, use it. And I thought that was, uh, or like, just show him what he, you know, why we were using it.And so, the amount of care that I think everyone has and the passion, appreciate, passion and appreciation for the moment. Right. This is a very unique time. So it's really cool to see everyone really like, uh, appreciate that.swyx: Yeah.Acquisition and DevEx Shiftswyx: One thing I wanted to do before we move over to sort of like research topics and, uh, the, the stuff that Kyle's working on is just tell the story of the acquisition, right?Like, not many people have been, been through an acquisition with Nvidia. What's it like? Uh, what, yeah, just anything you'd like to say.Nader: It's a crazy experience. I think, uh, you know, we were the thing that was the most exciting for us was. Our goal was just to make it easier for developers.We wanted to find access to GPUs, make it easier to do that. And then all, oh, actually your question about launchable. So launchable was just make one click exper, like one click deploys for any software on top of the GPU. Mm-hmm. And so what we really liked about Nvidia was that it felt like we just got a lot more resources to do all of that.I think, uh, you [00:08:00] know, NVIDIA's goal is to make things as easy for developers as possible. So there was a really nice like synergy there. I think that, you know, when it comes to like an acquisition, I think the amount that the soul of the products align, I think is gonna be. Is going speak to the success of the acquisition.Yeah. And so it in many ways feels like we're home. This is a really great outcome for us. Like we you know, I love brev.nvidia.com. Like you should, you should use it's, it's theKyle: front page for GPUs.Nader: Yeah. Yeah. If you want GP views,Kyle: you go there, getswyx: it there, and it's like internally is growing very quickly.I, I don't remember You said some stats there.Nader: Yeah, yeah, yeah. It's, uh, I, I wish I had the exact numbers, but like internally, externally, it's been growing really quickly. We've been working with a bunch of partners with a bunch of different customers and ISVs, if you have a solution that you want someone that runs on the GPU and you want people to use it quickly, we can bundle it up, uh, in a launchable and make it a one click run.If you're doing things and you want just like a sandbox or something to run on, right. Like open claw. Huge moment. Super exciting. Our, uh, and we'll talk into it more, but. You know, internally, people wanna run this, and you, we know we have to be really careful from the security implications. Do we let this run on the corporate network?Security's guidance was, Hey, [00:09:00] run this on breath, it's in, you know, it's, it's, it's a vm, it's sitting in the cloud, it's off the corporate network. It's isolated. And so that's been our stance internally and externally about how to even run something like open call while we figure out how to run these things securely.But yeah,swyx: I think there's also like, you almost like we're the right team at the right time when Nvidia is starting to invest a lot more in developer experience or whatever you call it. Yeah. Uh, UX or I don't know what you call it, like software. Like obviously NVIDIA is always invested in software, but like, there's like, this is like a different audience.Yeah. It's aNader: widerKyle: developer base.swyx: Yeah. Right.Nader: Yeah. Yeah. You know, it's funny, it's like, it's not, uh,swyx: so like, what, what is it called internally? What, what is this that people should be aware that is going on there?Nader: Uh, what, like developer experienceswyx: or, yeah, yeah. Is it's called just developer experience or is there like a broader strategy hereNader: in Nvidia?Um, Nvidia always wants to make a good developer experience. The thing is and a lot of the technology is just really complicated. Like, it's not, it's uh, you know, I think, um. The thing that's been really growing or the AI's growing is having a huge moment, not [00:10:00] because like, let's say data scientists in 2018, were quiet then and are much louder now.The pie is com, right? There's a whole bunch of new audiences. My mom's wondering what she's doing. My sister's learned, like taught herself how to code. Like the, um, you know, I, I actually think just generally AI's a big equalizer and you're seeing a more like technologically literate society, I guess.Like everyone's, everyone's learning how to code. Uh, there isn't really an excuse for that. And so building a good UX means that you really understand who your end user is. And when your end user becomes such a wide, uh, variety of people, then you have to almost like reinvent the practice, right? Yeah. You haveKyle: to, and actually build more developer ux, right?Because the, there are tiers of developer base that were added. You know, the, the hackers that are building on top of open claw, right? For example, have never used gpu. They don't know what kuda is. They, they, they just want to run something.Nader: Yeah.Kyle: You need new UX that is not just. Hey, you know, how do you program something in Cuda and run it?And then, and then we built, you know, like when Deep Learning was getting big, we built, we built Torch and, and, but so recently the amount of like [00:11:00] layers that are added to that developer stack has just exploded because AI has become ubiquitous. Everyone's using it in different ways. Yeah. It'sNader: moving fast in every direction.Vertical, horizontal.Vibhu: Yeah. You guys, you even take it down to hardware, like the DGX Spark, you know, it's, it's basically the same system as just throwing it up on big GPU cluster.Nader: Yeah, yeah, yeah. It's amazing. Blackwell.swyx: Yeah. Uh, we saw the preview at the last year's GTC and that was one of the better performing, uh, videos so far, and video coverage so far.Awesome. This will beat it. Um,Nader: that wasswyx: actually, we have fingersNader: crossed. Yeah.DGX Spark and Remote AccessNader: Even when Grace Blackwell or when, um, uh, DGX Spark was first coming out getting to be involved in that from the beginning of the developer experience. And it just comes back to what youswyx: were involved.Nader: Yeah. St. St.swyx: Mars.Nader: Yeah. Yeah. I mean from, it was just like, I, I got an email, we just got thrown into the loop and suddenly yeah, I, it was actually really funny ‘cause I'm still pretty fresh from the acquisition and I'm, I'm getting an email from a bunch of the engineering VPs about like, the new hardware, GPU chip, like we're, or not chip, but just GPU system that we're putting out.And I'm like, okay, cool. Matters. Now involved with this for the ux, I'm like. What am I gonna do [00:12:00] here? So, I remember the first meeting, I was just like kind of quiet as I was hearing engineering VPs talk about what this box could be, what it could do, how we should use it. And I remember, uh, one of the first ideas that people were idea was like, oh, the first thing that it was like, I think a quote was like, the first thing someone's gonna wanna do with this is get two of them and run a Kubernetes cluster on top of them.And I was like, oh, I think I know why I'm here. I was like, the first thing we're doing is easy. SSH into the machine. And then, and you know, just kind of like scoping it down of like, once you can do that every, you, like the person who wants to run a Kubernetes cluster onto Sparks has a higher propensity for pain, then, then you know someone who buys it and wants to run open Claw right now, right?If you can make sure that that's as effortless as possible, then the rest becomes easy. So there's a tool called Nvidia Sync. It just makes the SSH connection really simple. So, you know, if you think about it like. If you have a Mac, uh, or a PC or whatever, if you have a laptop and you buy this GPU and you want to use it, you should be able to use it like it's A-A-G-P-U in the cloud, right?Um, but there's all this friction of like, how do you actually get into that? That's part of [00:13:00] Revs value proposition is just, you know, there's a CLI that wraps SSH and makes it simple. And so our goal is just get you into that machine really easily. And one thing we just launched at CES, it's in, it's still in like early access.We're ironing out some kinks, but it should be ready by GTC. You can register your spark on Brev. And so now if youswyx: like remote managed yeah, local hardware. Single pane of glass. Yeah. Yeah. Because Brev can already manage other clouds anyway, right?Vibhu: Yeah, yeah. And you use the spark on Brev as well, right?Nader: Yeah. But yeah, exactly. So, so you, you, so you, you set it up at home you can run the command on it, and then it gets it's essentially it'll appear in your Brev account, and then you can take your laptop to a Starbucks or to a cafe, and you'll continue to use your, you can continue use your spark just like any other cloud node on Brev.Yeah. Yeah. And it's just like a pre-provisioned centerswyx: in yourNader: home. Yeah, exactly.swyx: Yeah. Yeah.Vibhu: Tiny little data center.Nader: Tiny little, the size ofVibhu: your phone.SOL Culture and Dynamo Setupswyx: One more thing before we move on to Kyle. Just have so many Jensen stories and I just love, love mining Jensen stories. Uh, my favorite so far is SOL. Uh, what is, yeah, what is S-O-L-S-O-LNader: is actually, i, I think [00:14:00] of all the lessons I've learned, that one's definitely my favorite.Kyle: It'll always stick with you.Nader: Yeah. Yeah. I, you know, in your startup, everything's existential, right? Like we've, we've run out of money. We were like, on the risk of, of losing payroll, we've had to contract our team because we l ran outta money. And so like, um, because of that you're really always forcing yourself to I to like understand the root cause of everything.If you get a date, if you get a timeline, you know exactly why that date or timeline is there. You're, you're pushing every boundary and like, you're not just say, you're not just accepting like a, a no. Just because. And so as you start to introduce more layers, as you start to become a much larger organization, SOL is is essentially like what is the physics, right?The speed of light moves at a certain speed. So if flight's moving some slower, then you know something's in the way. So before trying to like layer reality back in of like, why can't this be delivered at some date? Let's just understand the physics. What is the theoretical limit to like, uh, how fast this can go?And then start to tell me why. ‘cause otherwise people will start telling you why something can't be done. But actually I think any great leader's goal is just to create urgency. Yeah. [00:15:00] There's an infiniteKyle: create compelling events, right?Nader: Yeah.Kyle: Yeah. So l is a term video is used to instigate a compelling event.You say this is done. How do we get there? What is the minimum? As much as necessary, as little as possible thing that it takes for us to get exactly here and. It helps you just break through a bunch of noise.swyx: Yeah.Kyle: Instantly.swyx: One thing I'm unclear about is, can only Jensen use the SOL card? Like, oh, no, no, no.Not everyone get the b******t out because obviously it's Jensen, but like, can someone else be like, no, likeKyle: frontline engineers use it.Nader: Yeah. Every, I think it's not so much about like, get the b******t out. It's like, it's like, give me the root understanding, right? Like, if you tell me something takes three weeks, it like, well, what's the first principles?Yeah, the first principles. It's like, what's the, what? Like why is it three weeks? What is the actual yeah. What's the actual limit of why this is gonna take three weeks? If you're gonna, if you, if let's say you wanted to buy a new computer and someone told you it's gonna be here in five days, what's the SOL?Well, like the SOL is like, I could walk into a Best Buy and pick it up for you. Right? So then anything that's like beyond that is, and is that practical? Is that how we're gonna, you know, let's say give everyone in the [00:16:00] company a laptop, like obviously not. So then like that's the SOL and then it's like, okay, well if we have to get more than 10, suddenly there might be some, right?And so now we can kind of piece the reality back.swyx: So, so this is the. Paul Graham do things that don't scale. Yeah. And this is also the, what people would now call behi agency. Yeah.Kyle: It's actually really interesting because there's a, there's a second hardware angle to SOL that like doesn't come up for all the org sol is used like culturally at aswyx: media for everything.I'm also mining for like, I think that can be annoying sometimes. And like someone keeps going IOO you and you're like, guys, like we have to be stable. We have to, we to f*****g plan. Yeah.Kyle: It's an interesting balance.Nader: Yeah. I encounter that with like, actually just with, with Alec, right? ‘cause we, we have a new conference so we need to launch, we have, we have goals of what we wanna launch by, uh, by the conference and like, yeah.At the end of the day, where isswyx: this GTC?Nader: Um, well this is like, so we, I mean we did it for CES, we did for GT CDC before that we're doing it for GTC San Jose. So I mean, like every, you know, we have a new moment. Um, and we want to launch something. Yeah. And we want to do so at SOL and that does mean that some, there's some level of prioritization that needs [00:17:00] to happen.And so it, it is difficult, right? I think, um, you have to be careful with what you're pushing. You know, stability is important and that should be factored into S-O-L-S-O-L isn't just like, build everything and let it break, you know, that, that's part of the conversation. So as you're laying, layering in all the details, one of them might be, Hey, we could build this, but then it's not gonna be stable for X, y, z reasons.And so that was like, one of our conversations for CES was, you know, hey, like we, we can get this into early access registering your spark with brev. But there are a lot of things that we need to do in order to feel really comfortable from a security perspective, right? There's a lot of networking involved before we deliver that to users.So it's like, okay. Let's get this to a point where we can at least let people experiment with it. We had it in a booth, we had it in Jensen's keynote, and then let's go iron out all the networking kinks. And that's not easy. And so, uh, that can come later. And so that was the way that we layered that back in.Yeah. ButKyle: It's not really about saying like, you don't have to do the, the maintenance or operational work. It's more about saying, you know, it's kind of like [00:18:00] highlights how progress is incremental, right? Like, what is the minimum thing that we can get to. And then there's SOL for like every component after that.But there's the SOL to get you, get you to the, the starting line. And that, that's usually how it's asked. Yeah. On the other side, you know, like SOL came out of like hardware at Nvidia. Right. So SOL is like literally if we ran the accelerator or the GPU with like at basically full speed with like no other constraints, like how FAST would be able to make a program go.swyx: Yeah. Yeah. Right.Kyle: Soswyx: in, in training that like, you know, then you work back to like some percentage of like MFU for example.Kyle: Yeah, that's a, that's a great example. So like, there's an, there's an S-O-L-M-F-U, and then there's like, you know, what's practically achievable.swyx: Cool. Should we move on to sort of, uh, Kyle's side?Uh, Kyle, you're coming more from the data science world. And, uh, I, I mean I always, whenever, whenever I meet someone who's done working in tabular stuff, graph neural networks, time series, these are basically when I go to new reps, I go to ICML, I walk the back halls. There's always like a small group of graph people.Yes. Absolute small group of tabular people. [00:19:00] And like, there's no one there. And like, it's very like, you know what I mean? Like, yeah, no, like it's, it's important interesting work if you care about solving the problems that they solve.Kyle: Yeah.swyx: But everyone else is just LMS all the time.Kyle: Yeah. I mean it's like, it's like the black hole, right?Has the event horizon reached this yet in nerves? Um,swyx: but like, you know, those are, those are transformers too. Yeah. And, and those are also like interesting things. Anyway, uh, I just wanted to spend a little bit of time on, on those, that background before we go into Dynamo, uh, proper.Kyle: Yeah, sure. I took a different path to Nvidia than that, or I joined six years ago, seven, if you count, when I was an intern.So I joined Nvidia, like right outta college. And the first thing I jumped into was not what I'd done in, during internship, which was like, you know, like some stuff for autonomous vehicles, like heavyweight object detection. I jumped into like, you know, something, I'm like, recommenders, this is popular. Andswyx: yeah, he did RexiKyle: as well.Yeah, Rexi. Yeah. I mean that, that was the taboo data at the time, right? You have tables of like, audience qualities and item qualities, and you're trying to figure out like which member of [00:20:00] the audience matches which item or, or more practically which item matches which member of the audience. And at the time, really it was like we were trying to enable.Uh, recommender, which had historically been like a little bit of a CP based workflow into something that like, ran really well in GPUs. And it's since been done. Like there are a bunch of libraries for Axis that run on GPUs. Uh, the common models like Deeplearning recommendation model, which came outta meta and the wide and deep model, which was used or was released by Google were very accelerated by GPUs using, you know, the fast HBM on the chips, especially to do, you know, vector lookups.But it was very interesting at the time and super, super relevant because like we were starting to get like. This explosion of feeds and things that required rec recommenders to just actively be on all the time. And sort of transitioned that a little bit towards graph neural networks when I discovered them because I was like, okay, you can actually use graphical neural networks to represent like, relationships between people, items, concepts, and that, that interested me.So I jumped into that at [00:21:00] Nvidia and, and got really involved for like two-ish years.swyx: Yeah. Uh, and something I learned from Brian Zaro Yeah. Is that you can just kind of choose your own path in Nvidia.Kyle: Oh my God. Yeah.swyx: Which is not a normal big Corp thing. Yeah. Like you, you have a lane, you stay in your lane.Nader: I think probably the reason why I enjoy being in a, a big company, the mission is the boss probably from a startup guy. Yeah. The missionswyx: is the boss.Nader: Yeah. Uh, it feels like a big game of pickup basketball. Like, you know, if you play one, if you wanna play basketball, you just go up to the court and you're like, Hey look, we're gonna play this game and we need three.Yeah. And you just like find your three. That's honestly for every new initiative that's what it feels like. Yeah.Vibhu: It also like shows, right? Like Nvidia. Just releasing state-of-the-art stuff in every domain. Yeah. Like, okay, you expect foundation models with Nemo tron voice just randomly parakeet.Call parakeet just comes out another one, uh, voice. TheKyle: video voice team has always been producing.Vibhu: Yeah. There's always just every other domain of paper that comes out, dataset that comes out. It's like, I mean, it also stems back to what Nvidia has to do, right? You have to make chips years before they're actually produced.Right? So you need to know, you need to really [00:22:00] focus. TheKyle: design process starts likeVibhu: exactlyKyle: three to five years before the chip gets to the market.Vibhu: Yeah. I, I'm curious more about what that's like, right? So like, you have specialist teams. Is it just like, you know, people find an interest, you go in, you go deep on whatever, and that kind of feeds back into, you know, okay, we, we expect predictions.Like the internals at Nvidia must be crazy. Right? You know? Yeah. Yeah. You know, you, you must. Not even without selling to people, you have your own predictions of where things are going. Yeah. And they're very based, very grounded. Right?Kyle: Yeah. It, it, it's really interesting. So there's like two things that I think that Amed does, which are quite interesting.Uh, one is like, we really index into passion. There's a big. Sort of organizational top sound push to like ensure that people are working on the things that they're passionate about. So if someone proposes something that's interesting, many times they can just email someone like way up the chain that they would find this relevant and say like, Hey, can I go work on this?Nader: It's actually like I worked at a, a big company for a couple years before, uh, starting on my startup journey and like, it felt very weird if you were to like email out of chain, if that makes [00:23:00] sense. Yeah. The emails at Nvidia are like mosh pitsswyx: shoot,Nader: and it's just like 60 people, just whatever. And like they're, there's this,swyx: they got messy like, reply all you,Nader: oh, it's in, it's insane.It's insane. They justKyle: help. You know, Maxim,Nader: the context. But, but that's actually like, I've actually, so this is a weird thing where I used to be like, why would we send emails? We have Slack. I am the entire, I'm the exact opposite. I feel so bad for anyone who's like messaging me on Slack ‘cause I'm so unresponsive.swyx: Your emailNader: Maxi, email Maxim. I'm email maxing Now email is a different, email is perfect because man, we can't work together. I'm email is great, right? Because important threads get bumped back up, right? Yeah, yeah. Um, and so Slack doesn't do that. So I just have like this casino going off on the right or on the left and like, I don't know which thread was from where or what, but like the threads get And then also just like the subject, so you can have like working threads.I think what's difficult is like when you're small, if you're just not 40,000 people I think Slack will work fine, but there's, I don't know what the inflection point is. There is gonna be a point where that becomes really messy and you'll actually prefer having email. ‘cause you can have working threads.You can cc more than nine people in a thread.Kyle: You can fork stuff.Nader: You can [00:24:00] fork stuff, which is super nice and just like y Yeah. And so, but that is part of where you can propose a plan. You can also just. Start, honestly, momentum's the only authority, right? So like, if you can just start, start to make a little bit of progress and show someone something, and then they can try it.That's, I think what's been, you know, I think the most effective way to push anything for forward. And that's both at Nvidia and I think just generally.Kyle: Yeah, there's, there's the other concept that like is explored a lot at Nvidia, which is this idea of a zero billion dollar business. Like market creation is a big thing at Nvidia.Like,swyx: oh, you want to go and start a zero billion dollar business?Kyle: Jensen says, we are completely happy investing in zero billion dollar markets. We don't care if this creates revenue. It's important for us to know about this market. We think it will be important in the future. It can be zero billion dollars for a while.I'm probably minging as words here for, but like, you know, like, I'll give an example. NVIDIA's been working on autonomous driving for a a long time,swyx: like an Nvidia car.Kyle: No, they, they'veVibhu: used the Mercedes, right? They're around the HQ and I think it finally just got licensed out. Now they're starting to be used quite a [00:25:00] bit.For 10 years you've been seeing Mercedes with Nvidia logos driving.Kyle: If you're in like the South San Santa Clara, it's, it's actually from South. Yeah. So, um. Zero billion dollar markets are, are a thing like, you know, Jensen,swyx: I mean, okay, look, cars are not a zero billion dollar market. But yeah, that's a bad example.Nader: I think, I think he's, he's messaging, uh, zero today, but, or even like internally, right? Like, like it's like, uh, an org doesn't have to ruthlessly find revenue very quickly to justify their existence. Right. Like a lot of the important research, a lot of the important technology being developed that, that's kind ofKyle: where research, research is very ide ideologically free at Nvidia.Yeah. Like they can pursue things that they wereswyx: Were you research officially?Kyle: I was never in research. Officially. I was always in engineering. Yeah. We in, I'm in an org called Deep Warning Algorithms, which is basically just how do we make things that are relevant to deep warning go fast.swyx: That sounds freaking cool.Vibhu: And I think a lot of that is underappreciated, right? Like time series. This week Google put out time. FF paper. Yeah. A new time series, paper res. Uh, Symantec, ID [00:26:00] started applying Transformers LMS to Yes. Rec system. Yes. And when you think the scale of companies deploying these right. Amazon recommendations, Google web search, it's like, it's huge scale andKyle: Yeah.Vibhu: You want fast?Kyle: Yeah. Yeah. Yeah. Actually it's, it, I, there's a fun moment that brought me like full circle. Like, uh, Amazon Ads recently gave a talk where they talked about using Dynamo for generative recommendation, which was like super, like weirdly cathartic for me. I'm like, oh my God. I've, I've supplanted what I was working on.Like, I, you're using LMS now to do what I was doing five years ago.swyx: Yeah. Amazing. And let's go right into Dynamo. Uh, maybe introduce Yeah, sure. To the top down and Yeah.Kyle: I think at this point a lot of people are familiar with the term of inference. Like funnily enough, like I went from, you know, inference being like a really niche topic to being something that's like discussed on like normal people's Twitter feeds.It's,Nader: it's on billboardsKyle: here now. Yeah. Very, very strange. Driving, driving, seeing just an inference ad on 1 0 1 inference at scale is becoming a lot more important. Uh, we have these moments like, you know, open claw where you have these [00:27:00] agents that take lots and lots of tokens, but produce, incredible results.There are many different aspects of test time scaling so that, you know, you can use more inference to generate a better result than if you were to use like a short amount of inference. There's reasoning, there's quiring, there's, adding agency to the model, allowing it to call tools and use skills.Dyno sort came about at Nvidia. Because myself and a couple others were, were sort of talking about the, these concepts that like, you know, you have inference engines like VLMS, shelan, tenor, TLM and they have like one single copy. They, they, they sort of think about like things as like one single copy, like one replica, right?Why Scale Out WinsKyle: Like one version of the model. But when you're actually serving things at scale, you can't just scale up that replica because you end up with like performance problems. There's a scaling limit to scaling up replicas. So you actually have to scale out to use a, maybe some Kubernetes type terminology.We kind of realized that there was like. A lot of potential optimization that we could do in scaling out and building systems for data [00:28:00] center scale inference. So Dynamo is this data center scale inference engine that sits on top of the frameworks like VLM Shilling and 10 T lm and just makes things go faster because you can leverage the economy of scale.The fact that you have KV cash, which we can define a little bit later, uh, in all these machines that is like unique and you wanna figure out like the ways to maximize your cash hits or you want to employ new techniques in inference like disaggregation, which Dynamo had introduced to the world in, in, in March, not introduced, it was a academic talk, but beforehand.But we are, you know, one of the first frameworks to start, supporting it. And we wanna like, sort of combine all these techniques into sort of a modular framework that allows you to. Accelerate your inference at scale.Nader: By the way, Kyle and I became friends on my first date, Nvidia, and I always loved, ‘cause like he always teaches meswyx: new things.Yeah. By the way, this is why I wanted to put two of you together. I was like, yeah, this is, this is gonna beKyle: good. It's very, it's very different, you know, like we've, we, we've, we've talked to each other a bunch [00:29:00] actually, you asked like, why, why can't we scale up?Nader: Yeah.Scale Up Limits ExplainedNader: model, you said model replicas.Kyle: Yeah. So you, so scale up means assigning moreswyx: heavier?Kyle: Yeah, heavier. Like making things heavier. Yeah, adding more GPUs. Adding more CPUs. Scale out is just like having a barrier saying, I'm gonna duplicate my representation of the model or a representation of this microservice or something, and I'm gonna like, replicate it Many times.Handle, load. And the reason that you can't scale, scale up, uh, past some points is like, you know, there, there, there are sort of hardware bounds and algorithmic bounds on, on that type of scaling. So I'll give you a good example that's like very trivial. Let's say you're on an H 100. The Maxim ENV link domain for H 100, for most Ds H one hundreds is heus, right?So if you scaled up past that, you're gonna have to figure out ways to handle the fact that now for the GPUs to communicate, you have to do it over Infin band, which is still very fast, but is not as fast as ENV link.swyx: Is it like one order of magnitude, like hundreds or,Kyle: it's about an order of magnitude?Yeah. Okay. Um, soswyx: not terrible.Kyle: [00:30:00] Yeah. I, I need to, I need to remember the, the data sheet here, like, I think it's like about 500 gigabytes. Uh, a second unidirectional for ENV link, and about 50 gigabytes a second unidirectional for Infin Band. I, it, it depends on the, the generation.swyx: I just wanna set this up for people who are not familiar with these kinds of like layers and the trash speedVibhu: and all that.Of course.From Laptop to Multi NodeVibhu: Also, maybe even just going like a few steps back before that, like most people are very familiar with. You see a, you know, you can use on your laptop, whatever these steel viol, lm you can just run inference there. All, there's all, you can, youcan run it on thatVibhu: laptop. You can run on laptop.Then you get to, okay, uh, models got pretty big, right? JLM five, they doubled the size, so mm-hmm. Uh, what do you do when you have to go from, okay, I can get 128 gigs of memory. I can run it on a spark. Then you have to go multi GPU. Yeah. Okay. Multi GPU, there's some support there. Now, if I'm a company and I don't have like.I'm not hiring the best researchers for this. Right. But I need to go [00:31:00] multi-node, right? I have a lot of servers. Okay, now there's efficiency problems, right? You can have multiple eight H 100 nodes, but, you know, is that as a, like, how do you do that efficiently?Kyle: Yeah. How do you like represent them? How do you choose how to represent the model?Yeah, exactly right. That's a, that's like a hard question. Everyone asks, how do you size oh, I wanna run GLM five, which just came out new model. There have been like four of them in the past week, by the way, like a bunch of new models.swyx: You know why? Right? Deep seek.Kyle: No comment. Oh. Yeah, but Ggl, LM five, right?We, we have this, new model. It's, it's like a large size, and you have to figure out how to both scale up and scale out, right? Because you have to find the right representation that you care about. Everyone does this differently. Let's be very clear. Everyone figures this out in their own path.Nader: I feel like a lot of AI or ML even is like, is like this. I think people think, you know, I, I was, there was some tweet a few months ago that was like, why hasn't fine tuning as a service taken off? You know, that might be me. It might have been you. Yeah. But people want it to be such an easy recipe to follow.But even like if you look at an ML model and specificKyle: to you Yeah,Nader: yeah.Kyle: And the [00:32:00] model,Nader: the situation, and there's just so much tinkering, right? Like when you see a model that has however many experts in the ME model, it's like, why that many experts? I don't, they, you know, they tried a bunch of things and that one seemed to do better.I think when it comes to how you're serving inference, you know, you have a bunch of decisions to make and there you can always argue that you can take something and make it more optimal. But I think it's this internal calibration and appetite for continued calibration.Vibhu: Yeah. And that doesn't mean like, you know, people aren't taking a shot at this, like tinker from thinking machines, you know?Yeah. RL as a service. Yeah, totally. It's, it also gets even harder when you try to do big model training, right? We're not the best at training Moes, uh, when they're pre-trained. Like we saw this with LAMA three, right? They're trained in such a sparse way that meta knows there's gonna be a bunch of inference done on these, right?They'll open source it, but it's very trained for what meta infrastructure wants, right? They wanna, they wanna inference it a lot. Now the question to basically think about is, okay, say you wanna serve a chat application, a coding copilot, right? You're doing a layer of rl, you're serving a model for X amount of people.Is it a chat model, a coding model? Dynamo, you know, back to that,Kyle: it's [00:33:00] like, yeah, sorry. So you we, we sort of like jumped off of, you know, jumped, uh, on that topic. Everyone has like, their own, own journey.Cost Quality Latency TradeoffsKyle: And I, I like to think of it as defined by like, what is the model you need? What is the accuracy you need?Actually I talked to NA about this earlier. There's three axes you care about. What is the quality that you're able to produce? So like, are you accurate enough or can you complete the task with enough, performance, high enough performance. Yeah, yeah. Uh, there's cost. Can you serve the model or serve your workflow?Because it's not just the model anymore, it's the workflow. It's the multi turn with an agent cheaply enough. And then can you serve it fast enough? And we're seeing all three of these, like, play out, like we saw, we saw new models from OpenAI that you know, are faster. You have like these new fast versions of models.You can change the amount of thinking to change the amount of quality, right? Produce more tokens, but at a higher cost in a, in a higher latency. And really like when you start this journey of like trying to figure out how you wanna host a model, you, you, you think about three things. What is the model I need to serve?How many times do I need to call it? What is the input sequence link was [00:34:00] the, what does the workflow look like on top of it? What is the SLA, what is the latency SLA that I need to achieve? Because there's usually some, this is usually like a constant, you, you know, the SLA that you need to hit and then like you try and find the lowest cost version that hits all of these constraints.Usually, you know, you, you start with those things and you say you, you kind of do like a bit of experimentation across some common configurations. You change the tensor parallel size, which is a form of parallelismVibhu: I take, it goes even deeper first. Gotta think what model.Kyle: Yes, course,ofKyle: course. It's like, it's like a multi-step design process because as you said, you can, you can choose a smaller model and then do more test time scaling and it'll equate the quality of a larger model because you're doing the test time scaling or you're adding a harness or something.So yes, it, it goes way deeper than that. But from the performance perspective, like once you get to the model you need, you need to host, you look at that and you say, Hey. I have this model, I need to serve it at the speed. What is the right configuration for that?Nader: You guys see the recent, uh, there was a paper I just saw like a few days ago that, uh, if you run [00:35:00] the same prompt twice, you're getting like double Just try itagain.Nader: Yeah, exactly.Vibhu: And you get a lot. Yeah. But the, the key thing there is you give the context of the failed try, right? Yeah. So it takes a shot. And this has been like, you know, basic guidance for quite a while. Just try again. ‘cause you know, trying, just try again. Did you try again? All adviceNader: in life.Vibhu: Just, it's a paper from Google, if I'm not mistaken, right?Yeah,Vibhu: yeah. I think it, it's like a seven bas little short paper. Yeah. Yeah. The title's very cute. And it's just like, yeah, just try again. Give it ask context,Kyle: multi-shot. You just like, say like, hey, like, you know, like take, take a little bit more, take a little bit more information, try and fail. Fail.Vibhu: And that basic concept has gone pretty deep.There's like, um, self distillation, rl where you, you do self distillation, you do rl and you have past failure and you know, that gives some signal so people take, try it again. Not strong enough.swyx: Uh, for, for listeners, uh, who listen to here, uh, vivo actually, and I, and we run a second YouTube channel for our paper club where, oh, that's awesome.Vivo just covered this. Yeah. Awesome. Self desolation and all that's, that's why he, to speed [00:36:00] on it.Nader: I'll to check it out.swyx: Yeah. It, it's just a good practice, like everyone needs, like a paper club where like you just read papers together and the social pressure just kind of forces you to just,Nader: we, we,there'sNader: like a big inference.Kyle: ReadingNader: group at a video. I feel so bad every time. I I, he put it on like, on our, he shared it.swyx: One, one ofNader: your guys,swyx: uh, is, is big in that, I forget es han Yeah, yeah,Kyle: es Han's on my team. Actually. Funny. There's a, there's a, there's a employee transfer between us. Han worked for Nater at Brev, and now he, he's on my team.He wasNader: our head of ai. And then, yeah, once we got in, andswyx: because I'm always looking for like, okay, can, can I start at another podcast that only does that thing? Yeah. And, uh, Esan was like, I was trying to like nudge Esan into like, is there something here? I mean, I don't think there's, there's new infant techniques every day.So it's like, it's likeKyle: you would, you would actually be surprised, um, the amount of blog posts you see. And ifswyx: there's a period where it was like, Medusa hydra, what Eagle, like, youKyle: know, now we have new forms of decode, uh, we have new forms of specula, of decoding or new,swyx: what,Kyle: what are youVibhu: excited? And it's exciting when you guys put out something like Tron.‘cause I remember the paper on this Tron three, [00:37:00] uh, the amount of like post train, the on tokens that the GPU rich can just train on. And it, it was a hybrid state space model, right? Yeah.Kyle: It's co-designed for the hardware.Vibhu: Yeah, go design for the hardware. And one of the things was always, you know, the state space models don't scale as well when you do a conversion or whatever the performance.And you guys are like, no, just keep draining. And Nitron shows a lot of that. Yeah.Nader: Also, something cool about Nitron it was released in layers, if you will, very similar to Dynamo. It's, it's, it's essentially it was released as you can, the pre-training, post-training data sets are released. Yeah. The recipes on how to do it are released.The model itself is released. It's full model. You just benefit from us turning on the GPUs. But there are companies like, uh, ServiceNow took the dataset and they trained their own model and we were super excited and like, you know, celebrated that work.ZoomVibhu: different. Zoom is, zoom is CGI, I think, uh, you know, also just to add like a lot of models don't put out based models and if there's that, why is fine tuning not taken off?You know, you can do your own training. Yeah,Kyle: sure.Vibhu: You guys put out based model, I think you put out everything.Nader: I believe I know [00:38:00]swyx: about base. BasicallyVibhu: without baseswyx: basic can be cancelable.Vibhu: Yeah. Base can be cancelable.swyx: Yeah.Vibhu: Safety training.swyx: Did we get a full picture of dymo? I, I don't know if we, what,Nader: what I'd love is you, you mentioned the three axes like break it down of like, you know, what's prefilled decode and like what are the optimizations that we can get with Dynamo?Kyle: Yeah. That, that's, that's, that's a great point. So to summarize on that three axis problem, right, there are three things that determine whether or not something can be done with inference, cost, quality, latency, right? Dynamo is supposed to be there to provide you like the runtime that allows you to pull levers to, you know, mix it up and move around the parade of frontier or the preto surface that determines is this actually possible with inference And AI todayNader: gives you the knobs.Kyle: Yeah, exactly. It gives you the knobs.Disaggregation Prefill vs DecodeKyle: Uh, and one thing that like we, we use a lot in contemporary inference and is, you know, starting to like pick up from, you know, in, in general knowledge is this co concept of disaggregation. So historically. Models would be hosted with a single inference engine. And that inference engine [00:39:00] would ping pong between two phases.There's prefill where you're reading the sequence generating KV cache, which is basically just a set of vectors that represent the sequence. And then using that KV cache to generate new tokens, which is called Decode. And some brilliant researchers across multiple different papers essentially made the realization that if you separate these two phases, you actually gain some benefits.Those benefits are basically a you don't have to worry about step synchronous scheduling. So the way that an inference engine works is you do one step and then you finish it, and then you schedule, you start scheduling the next step there. It's not like fully asynchronous. And the problem with that is you would have, uh, essentially pre-fill and decode are, are actually very different in terms of both their resource requirements and their sometimes their runtime.So you would have like prefill that would like block decode steps because you, you'd still be pre-filing and you couldn't schedule because you know the step has to end. So you remove that scheduling issue and then you also allow you, or you yourself, to like [00:40:00] split the work into two different ki types of pools.So pre-fill typically, and, and this changes as, as model architecture changes. Pre-fill is, right now, compute bound most of the time with the sequence is sufficiently long. It's compute bound. On the decode side because you're doing a full Passover, all the weights and the entire sequence, every time you do a decode step and you're, you don't have the quadratic computation of KV cache, it's usually memory bound because you're retrieving a linear amount of memory and you're doing a linear amount of compute as opposed to prefill where you retrieve a linear amount of memory and then use a quadratic.You know,Nader: it's funny, someone exo Labs did a really cool demo where for the DGX Spark, which has a lot more compute, you can do the pre the compute hungry prefill on a DG X spark and then do the decode on a, on a Mac. Yeah. And soVibhu: that's faster.Nader: Yeah. Yeah.Kyle: So you could, you can do that. You can do machine strat stratification.Nader: Yeah.Kyle: And like with our future generation generations of hardware, we actually announced, like with Reuben, this [00:41:00] new accelerator that is prefilled specific. It's called Reuben, CPX. SoKubernetes Scaling with GroveNader: I have a question when you do the scale out. Yeah. Is scaling out easier with Dynamo? Because when you need a new node, you can dedicate it to either the Prefill or, uh, decode.Kyle: Yeah. So Dynamo actually has like a, a Kubernetes component in it called Grove that allows you to, to do this like crazy scaling specialization. It has like this hot, it's a representation that, I don't wanna go too deep into Kubernetes here, but there was a previous way that you would like launch multi-node work.Uh, it's called Leader Worker Set. It's in the Kubernetes standard, and Leader worker set is great. It served a lot of people super well for a long period of time. But one of the things that it's struggles with is representing a set of cases where you have a multi-node replica that has a pair, right?You know, prefill and decode, or it's not paired, but it has like a second stage that has a ratio that changes over time. And prefill and decode are like two different things as your workload changes, right? The amount of prefill you'll need to do may change. [00:42:00] The amount of decode that you, you'll need to do might change, right?Like, let's say you start getting like insanely long queries, right? That probably means that your prefill scales like harder because you're hitting these, this quadratic scaling growth.swyx: Yeah.And then for listeners, like prefill will be long input. Decode would be long output, for example, right?Kyle: Yeah. So like decode, decode scale. I mean, decode is funny because the amount of tokens that you produce scales with the output length, but the amount of work that you do per step scales with the amount of tokens in the context.swyx: Yes.Kyle: So both scales with the input and the output.swyx: That's true.Kyle: But on the pre-fold view code side, like if.Suddenly, like the amount of work you're doing on the decode side stays about the same or like scales a little bit, and then the prefilled side like jumps up a lot. You actually don't want that ratio to be the same. You want it to change over time. So Dynamo has a set of components that A, tell you how to scale.It tells you how many prefilled workers and decoded workers you, it thinks you should have, and also provides a scheduling API for Kubernetes that allows you to actually represent and affect this scheduling on, on, on your actual [00:43:00] hardware, on your compute infrastructure.Nader: Not gonna lie. I feel a little embarrassed for being proud of my SVG function earlier.swyx: No, itNader: wasreallyKyle: cute. I, Iswyx: likeNader: it's all,swyx: it's all engineering. It's all engineering. Um, that's where I'mKyle: technical.swyx: One thing I'm, I'm kind of just curious about with all with you see at a systems level, everything going on here. Mm-hmm. And we, you know, we're scaling it up in, in multi, in distributed systems.Context Length and Co Designswyx: Um, I think one thing that's like kind of, of the moment right now is people are asking, is there any SOL sort of upper bounds. In terms of like, let's call, just call it context length for one for of a better word, but you can break it down however you like.Nader: Yeah.swyx: I just think like, well, yeah, I mean, like clearly you can engage in hybrid architectures and throw in some state space models in there.All, all you want, but it looks, still looks very attention heavy.Kyle: Yes. Uh, yeah. Long context is attention heavy. I mean, we have these hybrid models, um,swyx: to take and most, most models like cap out at a million contexts and that's it. Yeah. Like for the last two years has been it.Kyle: Yeah. The model hardware context co-design thing that we're seeing these days is actually super [00:44:00] interesting.It's like my, my passion, like my secret side passion. We see models like Kimmy or G-P-T-O-S-S. I'm use these because I, I know specific things about these models. So Kimmy two comes out, right? And it's an interesting model. It's like, like a deep seek style architecture is MLA. It's basically deep seek, scaled like a little bit differently, um, and obviously trained differently as well.But they, they talked about, why they made the design choices for context. Kimmy has more experts, but fewer attention heads, and I believe a slightly smaller attention, uh, like dimension. But I need to remember, I need to check that. Uh, it doesn't matter. But they discussed this actually at length in a blog post on ji, which is like our pu which is like credit puswyx: Yeah.Kyle: Um, in, in China. Chinese red.swyx: Yeah.Kyle: It's, yeah. So it, it's, it's actually an incredible blog post. Uh, like all the mls people in, in, in that, I've seen that on GPU are like very brilliant, but they, they talk about like the creators of Kimi K two [00:45:00] actually like, talked about it on, on, on there in the blog post.And they say, we, we actually did an experiment, right? Attention scales with the number of heads, obviously. Like if you have 64 heads versus 32 heads, you do half the work of attention. You still scale quadratic, but you do half the work. And they made a, a very specific like. Sort of barter in their system, in their architecture, they basically said, Hey, what if we gave it more experts, so we're gonna use more memory capacity.But we keep the amount of activated experts the same. We increase the expert sparsity, so we have fewer experts act. The ratio to of experts activated to number of experts is smaller, and we decrease the number of attention heads.Vibhu: And kind of for context, what the, what we had been seeing was you make models sparser instead.So no one was really touching heads. You're just having, uh,Kyle: well, they, they did, they implicitly made it sparser.Vibhu: Yeah, yeah. For, for Kimmy. They did,Kyle: yes.Vibhu: They also made it sparser. But basically what we were seeing was people were at the level of, okay, there's a sparsity ratio. You want more total parameters, less active, and that's sparsity.[00:46:00]But what you see from papers, like, the labs like moonshot deep seek, they go to the level of, okay, outside of just number of experts, you can also change how many attention heads and less attention layers. More attention. Layers. Layers, yeah. Yes, yes. So, and that's all basically coming back to, just tied together is like hardware model, co-design, which isKyle: hardware model, co model, context, co-design.Vibhu: Yeah.Kyle: Right. Like if you were training a, a model that was like. Really, really short context, uh, or like really is good at super short context tasks. You may like design it in a way such that like you don't care about attention scaling because it hasn't hit that, like the turning point where like the quadratic curve takes over.Nader: How do you consider attention or context as a separate part of the co-design? Like I would imagine hardware or just how I would've thought of it is like hardware model. Co-design would be hardware model context co-designKyle: because the harness and the context that is produced by the harness is a part of the model.Once it's trained in,Vibhu: like even though towards the end you'll do long context, you're not changing architecture through I see. Training. Yeah.Kyle: I mean you can try.swyx: You're saying [00:47:00] everyone's training the harness into the model.Kyle: I would say to some degree, orswyx: there's co-design for harness. I know there's a small amount, but I feel like not everyone has like gone full send on this.Kyle: I think, I think I think it's important to internalize the harness that you think the model will be running. Running into the model.swyx: Yeah. Interesting. Okay. Bash is like the universal harness,Kyle: right? Like I'll, I'll give. An example here, right? I mean, or just like a, like a, it's easy proof, right? If you can train against a harness and you're using that harness for everything, wouldn't you just train with the harness to ensure that you get the best possible quality out of,swyx: Well, the, uh, I, I can provide a counter argument.Yeah, sure. Which is what you wanna provide a generally useful model for other people to plug into their harnesses, right? So if youKyle: Yeah. Harnesses can be open, open source, right?swyx: Yeah. So I mean, that's, that's effectively what's happening with Codex.Kyle: Yeah.swyx: And, but like you may want like a different search tool and then you may have to name it differently or,Nader: I don't know how much people have pushed on this, but can you.Train a model, would it be, have you have people compared training a model for the for the harness versus [00:48:00] like post training forswyx: I think it's the same thing. It's the same thing. It's okay. Just extra post training. INader: see.swyx: And so, I mean, cognition does this course, it does this where you, you just have to like, if your tool is slightly different, um, either force your tool to be like the tool that they train for.Hmm. Or undo their training for their tool and then Oh, that's re retrain. Yeah. It's, it's really annoying and like,Kyle: I would hope that eventually we hit like a certain level of generality with respect to training newswyx: tools. This is not a GI like, it's, this is a really stupid like. Learn my tool b***h.Like, I don't know if, I don't know if I can say that, but like, you know, um, I think what my point kind of is, is that there's, like, I look at slopes of the scaling laws and like, this slope is not working, man. We, we are at a million token con

Most people who end up in VFX spent years obsessing over frames and film. Ryan Kelsey spent 13 years in telecom in Cincinnati, selling fiber and managed IT services, before stumbling into an industry where studios win Oscars and go bankrupt in the same month. That collision of worlds turns out to be exactly the perspective the business needs right now. Ryan is VP of Sales at Center Grid Virtual Studio, and his outsider's eye cuts through a lot of the noise around cloud infrastructure for creative studios. Why are small VFX shops still running overheating GPU racks in their back offices? Why does a freelancer getting a big render job have nowhere obvious to turn? Why does everyone talk about AI compute without knowing what they're actually doing with it? This conversation, recorded live at the HPA (Hollywood Professional Association) Tech Retreat, ranges from the broken economics of fixed-bid VFX work to what a genuinely boutique cloud partner looks like compared to the AWS-sized behemoths, to Chris's teenage son dragging his friends to see Chainsaw Man while the industry insists nobody goes to the movies anymore. Links: Ryan Kelsey LinkedIn > Center Grid Virtual Studio > HPA Tech Retreat >  Scott Ross book > This episode is sponsored by: Center Grid Virtual Studio Kitbash 3D (Use promocode "cggarage" for 10% off)  

In this episode, Dillon dives into what it really takes to run an engineering firm working on mega projects like data centers. He explores the massive challenges around procurement, lead times, and electrical gear shortages in today's infrastructure boom—and reveals why the biggest bottleneck isn't design or labor, it's decision-making speed.Using XAI's rapid data center build as a case study, Dillon breaks down how Elon Musk's team achieved what seemed impossible: a 100,000 GPU cluster built in record time. The secret? Eliminating procurement bureaucracy and empowering people to say "yes" quickly.Learn why paying 5% more to accelerate timelines might be worth 50 million dollars, how single-task focus drives real productivity, and why the ability to make fast decisions is the ultimate competitive advantage in billion-dollar builds.

We just passed the 25th anniversary of the GeForce 3, which felt like a good reason to dust off the April 2001 issue of Maximum PC. We reflect on both a quarter-century of programmable pixel shaders -- the tech that's defined 3D rendering ever since -- and Will's cover story on the new GPU, including the secretive trip to Nvidia to benchmark it, a random Tim Sweeney interview, and more. There's also plenty of other fun retro tech to dish about in here, including super-early home wi-fi devices, the reveal of Windows XP, Pentium 4 RD-RAM weirdness, some classic Gordon Mah Ung hijinks, and more. The Maximum PC issue for this episode: https://archive.org/details/maximum-pc-the-nearly-complete-collection/Maximum%20PC/2001/031%20Maximum%20PC%204-1-2001/page/n1/mode/2up A clip of the Jack Matthews Metroid Prime interview (full interview also on the channel): https://www.youtube.com/watch?v=0oiIm5Ymu6s Support the Pod! Contribute to the Tech Pod Patreon and get access to our booming Discord, a monthly bonus episode, your name in the credits, and other great benefits! You can support the show at: https://patreon.com/techpod

Rethinking AI Compute Infrastructure: The TensorWave ApproachIn this episode, Jeff Tatarchuk, co-founder of TensorWave, shares how his deep industry experience and innovative mindset are transforming AI compute infrastructure. We explore how building specialized data centers, focusing on AMD GPUs, and creating flexible ecosystems are shaping the future of scalable AI.In this episode:The evolution of cloud companies and the rise of Neo clouds focused on AI computeTensorWave's unique strategy of deploying AMD GPUs in custom data centersLessons learned from FPGA cloud business and transitioning into GPU infrastructureThe technical challenges and solutions in scaling data centers quickly amidst power and supply chain constraintsThe importance of software ecosystems, interoperability, and supporting AMD's software stackHow TensorWave differentiates itself from purely financial arbitrage models and pure Nvidia-centric cloudsAMD's advantages in memory capacity, chiplet architecture, and software supportThe technical intricacies of CUDA versus ROCm, and efforts to build an open ecosystemFuture vision: democratized, reliable, and flexible AI compute options for enterprise and labsTimestamps:00:00 – Introduction to TensorWave and the AI compute landscape02:30 – The rise of Neo clouds and innovation waves in cloud infrastructure06:00 – How TensorWave's FPGA cloud background shaped its GPU strategy10:00 – Challenges in deploying large data centers: power, supply chain, and permitting14:00 – Building and scaling AMD GPU data centers quickly and efficiently19:00 – Software ecosystems: the CUDA moat and TensorWave's ‘Beyond CUDA' summit23:00 – Market differentiation: technical and operational challenges in the Neo cloud space27:00 – Supporting enterprise fine tuning and large-scale training demands32:00 – AMD's technical advantages: VRAM, chiplet architecture, and software support36:00 – Building an open, heterogeneous AI ecosystem beyond CUDA40:00 – What success looks like: a resilient, accessible AI compute futureResources & Links:⁠TensorWave⁠⁠Beyond CUDA Summit⁠⁠Scalar LM by Greg De Almos⁠⁠AMD MI300X Data Center Chip⁠⁠Nvidia H100⁠⁠RoCM Software Stack⁠⁠LinkedIn⁠⁠Twitter⁠This conversation offers a strategic look at how focused infrastructure development, software ecosystem support, and hardware differentiation are critical in shaping the future of accessible, scalable AI compute. Whether you're building data centers, developing AI hardware, or just interested in industry shifts, this episode provides valuable insights into how companies like TensorWave are reshaping the landscape.

A Zoomer arrested for stealing $46M from the US Marshals, Kraken makes history with a Fed Master Account, and IREN builds to 150,000 GPUs. Get your tickets to OPNEXT 2026 before prices increase! Join us on April 16 in NYC for technical discussions, investor talks, and intimate conversation with the brightest minds in Bitcoin. Chris Johhansen of Ion Stream and Kaan Farahani of Luxor join us to talk about the insane arrest of John DeGuida for allegedly stealing $46 million from the US Marshals Service. We break down Kraken Financial's historic Fed Master Account and what a "skinny" seat at the table means for the industry. Plus, we analyze the massive pivot from ASICs to GPUs and review the tumultuous Bitcoin hash rate data from February. Subscribe to the newsletter! https://newsletter.blockspacemedia.com Notes: * Zoomer stole $46M from US Marshals Service (his dad!) * Kraken gets first Fed Master Account. * Iren expanding GPU fleet to 150,000. * Difficulty adjustment targeting 7.5% up. Timestamps: 00:00 Start 04:53 Difficulty Report by Hashrate Index 07:49 $46M Stolen from US Marshals Service 15:35 Kraken Financial Granted Federal Reserve Master Account 21:48 AI Compute & Neocloud Dynamics 24:11 AI boom vs crypto boom 27:39 AI inference vs training 30:44 Scoping AI deals 32:41 H100 are still viable? 36:35 Hashrate 37:46 February suprises 44:40 What ASICs are profitable? 45:36 More hashrate declines? 47:36 5 cents per KWH 49:29 Hashrate prediction 52:51 IREN Expands GPU Fleet 1:01:44 Cry Corner: Miners Are Dumping BTC?

In the second of our two-part panel discussion from Morgan Stanley's TMT conference, our analysts break down the complexity of financing AI's infrastructure and the technological disruption happening across industries.Read more insights from Morgan Stanley.----- Transcript -----Michelle Weaver: Welcome back to Thoughts on the Market, and welcome to part two of our conversation live from the Technology, Media and Telecom conference. I'm Michelle Weaver, U.S. Thematic and Equity Strategist at Morgan Stanley. Today we're continuing our conversation with Stephen Byrd, Josh Baer and Lindsay Tyler. This time looking at financing AI and some of the risks to the story. It's Friday, March 6th at 11am in San Francisco. So yesterday we spoke about AI adoption. And while there's a lot of excitement on this theme, there've also been some concerns bubbling up. Lindsay, I want to start with you around financing. That's another critical component of the AI build out. What's your latest on the magnitude of the data center financing gap, and what role [are] credit markets playing here? Lindsay Tyler: Yeah, in partnership with Thematic Research, Stephen and team, and colleagues across fixed income research last summer, we did put out a note, thinking about the data center financing gap, right? So, Stephen and team modeled a $3 trillion global data center CapEx need over a four-year timeframe. So, in partnership with fixed income across asset classes, we thought: okay, how will that really be funded? And we came to the conclusion that the hyperscalers, the high quality hyperscalers, generate a good amount of cash flow, right? So, there's cash from ops that can fund approximately half of that. But then we think that fixed income markets are critical to fund the rest of the funding gap. And really private credit is the leader in that and then aided by corporate credit and also securitized credit. What we've seen since is that yes, private credit has served a role. There is this difference between private credit 1.0, which is more of that middle market direct lending. And then private credit 2.0, which is more ABF – Asset Based Finance or Asset Backed Finance. And what we see there is an interest in leases of hyperscaler tenants, right? We've also seen in the market over the past nine months or so, investment grade bond issuance by hyperscalers. Obviously, a use of cash flow by hyperscalers. We've seen the construction loans with banks and also private credit per reports. We've also seen high yield bond issuance, which is kind of a new trend for construction financing. We've seen ABS and CMBS as well. And then something new that's emerging in focus for investors is more of a chip-backed or compute contract backed financings, like more creative solutions. We're really in early innings of the spend right now. And so, there is this shift. As we start to work through the construction early phases, the next focus is: okay, but what about the chips? And so, I think a big focus is that, you know, chips are more than 50 percent of the spend for if you're looking at a gigawatt site. And it depends what type of chips and kind of what generation. But that's the next leg of this too. So, it's kind of a focus, you know, for 2026. Michelle Weaver: And how do you view balance sheet leverage and financing when you think about hyperscaler debt raising magnitude and timelines? Lindsay Tyler: So just to bring it down to more of a basic level, if you need compute, you really might need two things, right? A powered shell and then the chips. And so, if you're looking for that compute, you could kind of go in three basic ways. You could look to build the shell and kind of build and buy the whole thing. You could lease the shell, from, you know, a developer, maybe a Bitcoin miner too – that is converted to HBC. And then you kind of buy the chips and you put them in yourselves. Or you could lease all the compute; quote unquote lease, it's more of a contract. In terms of the funding, if you're thinking about the cash flows of some of the big companies – think of that as primarily being put towards chip spend. If you're thinking about the construction that's kind of split between cash CapEx but also leases. And so, what we've seen is that there is more than [$]600 billion of un-commenced lease obligations that will commence over the next two to five years, across the big four or five players. And then my equity counterparts estimate around [$]700 billion of cash CapEx that needs this year for some of those players as well. So, these are big numbers. But that's kind of how, at a basic level, they're approaching some of the financing. It's a split approach. Michelle Weaver: And what have you learned around financing the past few days at the conference? Anything incremental to share there? Lindsay Tyler: Sure. Yeah. I think I found confirmation of some key themes here at the conference. The first being that numerous funding buckets are available. That was a big focus of our note last year is that you can kind of look at asset level financing. You can look at public bonds, you can look at some equity. There are these different funding buckets available.The second is that tenant quality matters for construction financing. I think I've seen this more in the markets than maybe at this conference over the past two to three weeks. But that has been a focus of pricing for the deals, but also market depth for the deals. A third confirmation of a key theme was around the neo clouds and also the GPU as a service business models. Thinking about those creative financings, right. Are they thinking about from their compute counterparties? Would they like upfront payments? Might they look to move financing off [the] balance sheet, if they have a very high-quality investment grade rated counterparty? So, there is some of this evolution around those solutions. And then a fourth key theme is just around the credit support. And Stephen has and I have talked about this around some of the Bitcoin miners – is that, you know, there can be these higher quality investment grade players that might look to lend their credit support. Maybe a lease backstop to other players in the ecosystem in order to get a better pricing on construction financing. And we are seeing some press pickup around how that might play out in chip financing down the road too. Michelle Weaver: Mm-hmm. AI driven risk and potential disruption has been a big feature of the price action we've seen year-to-date in this theme. Stephen, what are some asset classes or businesses you see as resistant to some of this disruption? Stephen Byrd: We spend a lot of time thinking about, sort of, asset classes that are resistant to deflation and disruption. And what's interesting is there's actually a handful of economists in the world that are doing remarkable work on this concept. That they would call it the economics of transformative AI. There are three Americans, two Canadians, two Brits, a number of others who are doing really, really interesting work. And essentially what they're looking at is what do economies look like? As we see very powerful AI enter many industries – cause price reductions, deflation… What does that do? They have a lot of interesting takeaways, but one is this idea that the relative value of assets that cannot be deflated by AI goes up. Very simple idea. But think of it this way, I mean, there's only, you know, one principle resort on Kauai. You know, there's a limited amount of metals. And so, what we go through is this list that's gotten a lot of investor attention of resistant asset classes or more of the resistant asset classes that can go up in value. So, there are obvious ones like land, though you have to be a little careful with real estate in the sense that like, office real estate probably wouldn't be where you would go. Nor would you potentially go sort of towards middle income, lower income housing. But more, you know, think of industrial REITs, higher-end real estate. But there are a lot of other categories that are interesting to me. All kinds of infrastructure should be quite resistant, all kinds of critical materials. Metals should do extremely well in this. But then when you go beyond that, it's actually kind of interesting that there; arguably there's a longer list than those classic sort of land and metals examples.Examples here would be compute… Michelle Weaver: Mm-hmm. Stephen Byrd: I thought Jensen put it, well, you know, if there's a limited amount of infrastructure available, you want to put the best compute. And ultimately, in some ways, intelligence becomes the new coin of the realm in the world, right? So, I would want to own the purveyors of intelligence. It could include high-end luxury. It could include unique human experiences. So, I don't know how many of y'all have children who are sort of college age. But my children are college age, and they absolutely hate what they would call AI slop.They want legit human content, and they seek it out. And they absolutely hate it when they see bad copies of human content. And so, I think there is a place in many parts of the economy for unique human experiences, unique human content, and it's interesting to kind of seek out where that might be in the economy. So those would be some examples of resistant assets. Michelle Weaver: Mm-hmm. Josh, software's been at really the center of this AI disruption debate. How would you compare the current pullback in software multiples to prior periods of peak uncertainty? And do you think any of these concerns are valid? Or how are you thinking about that? Josh Baer: Great question. I mean, software multiples on an EV to sales basis are down 30 – 35 percent just from the fall, I will say. And that's overall in the group. A lot of stocks, multiple handfuls, are down 60-70 percent over the last year. And what's being priced in is really peak uncertainty, a lot of fear. And these multiples, now four times sales – takes us all the way back about 10 years to the shift to cloud. And this time in many ways reminds us of that period of peak fear. In this case, what's being priced in is terminal value risk. We talked about this TAM yesterday. But you know, who is going to win that share? How is it divided from a competitive perspective across these model providers? The LLMs with new entrants. Of course, the incumbents. And this other idea of in-housing. Michelle Weaver: Mm-hmm. Josh Baer: So, there's competitive risk, there's business model risk. Are companies going to need to change their pricing models from seat-based to consumption or hybrid. And then last margin risk. Just thinking about the higher input costs and higher capital intensity. And so, you know, all of those fears are being priced in right now. Michelle Weaver: And we, of course though, had a bunch of these companies live with us at the conference. How are they responding to some of these risks? How are they addressing these investor concerns? Josh Baer: Most of the companies here from our coverage are the incumbent software vendors. And I think that the leadership teams did a really nice job coming out and defending their competitive moats and really articulating the story of why they are in a great position to capitalize on the opportunity. And the reasons can vary across different companies. But some of the commonalities are around enterprise grade, trust, security, governance, acceptance from IT organizations.The idea of vibe coding all apps in an organization get squashed when you actually talk to companies and chief information officers. For some companies there's proprietary data moats, network effects. All of that's on top of existing customer relationships. And so, you know, that was the message from the companies that we had. That we're the incumbents. We get to use all of the same innovative AI technology in the same way that all these different competitive buckets do. But we have, you know, that differentiation in that moat. And so, we're in a good place. Michelle Weaver: I want to wrap on a positive note. Stephen, what did you hear at the conference that you're most excited about? Stephen Byrd: I'd say the life sciences. A few investors pointed out that perhaps AI has a PR problem these days. And I do think showing a significant benefit to humanity in terms of improved health outcomes, whether that's just better diagnosis, you know. Away from this event, but I was in India the week before and, you know, AI can have a powerful benefit to the people who suffer the most in terms of providing very powerful medical tools in a distributed manner. So, I'm a big fan there.But you know, in many ways, curing the most challenging diseases plaguing humanity. The kind of problems involved in providing those and developing those cures are perfect for AI. So that, for me – stepping way back – that is by far the most exciting thing. Michelle Weaver: Josh, same to you. What are you most excited about? Josh Baer: From my perspective, it's potentially the turning point for software. The ability to showcase that we are at this inflection point and acceleration. To actually see that it takes time for our software companies to develop new AI technologies. Put that into products that have been tested and proven and go through the enterprise adoption cycle. And that we're at the cusp of more adoption – that's what our survey work says. And to see that inflection, I think can help to rerate this sector. Michelle Weaver: Lindsay, same question for you… Lindsay Tyler: Maybe I'll tie it to markets. I've already had a lot of more conversations with equity investors over the past, how many months? There's a big fixed income focus right now, which is a great, you know, spot and really interesting opportunity in my seat. And there's a lot of interesting structures coming to be right now in the credit space. So, I think it's an exciting time. Michelle Weaver: Lindsay, Stephen, Josh, thank you very much for joining to recap the event and let us know what you learned at the conference. To our audience, thank you for listening here live. And to our audience tuning in, thanks for listening. If you enjoy Thoughts on the Market, please leave us a review wherever you listen. And share the podcast with a friend or colleague today.

On this week's episode of The MacRumors Show, we discuss Apple's concentrated week of announcements that saw the introduction of 10 new products.The most significant announcement of the week was the MacBook Neo, an all-new entry-level Apple laptop that starts at $599. The ‌MacBook Neo‌ is designed to compete with lower-cost Windows laptops and Chromebooks, while expanding the Mac lineup with a substantially more affordable option.Unlike every other Apple silicon Mac, the ‌MacBook Neo‌ is powered by the A18 Pro chip originally developed for the iPhone 16 Pro, making it the first Mac to use an iPhone-class processor instead of an M-series chip. The machine features a rounded, colorful design available in Silver, Indigo, Blush, and Citrus finishes, with matching keyboards and wallpapers that give it a more playful appearance than Apple's existing notebooks. At 2.7 pounds, it weighs the same as a MacBook Air.It offers a 13-inch Liquid Retina display with uniform, iPad-style bezels rather than a notch, a Magic Keyboard, a mechanical trackpad, two USB-C ports, 8GB of memory, a headphone jack, a 1080p camera, dual mics, dual speakers with Spatial Audio, and a battery life rated for up to 16 hours.Apple also updated several existing devices with modest specification improvements. The iPhone 17e retains the same design and price as the iPhone 16e but adds the A19 chip, MagSafe support, Apple's second-generation C1X modem, and 256GB of base storage.The 11- and 13-inch iPad Air gained the M4 chip, 12GB of RAM, Wi-Fi 7 support via Apple's N1 wireless chip, and the same C1X modem in cellular models. Meanwhile, the 13- and 15-inch ‌MacBook Air‌ were upgraded with the M5 chip and a higher base storage capacity of 512GB, though the removal of the 256GB option increased the starting price to $1,099.At the high end of the Mac lineup, Apple refreshed the 14-inch and 16-inch MacBook Pro models with the new M5 Pro and M5 Max chips, introducing a "Fusion Architecture" that bonds two 3nmdies together into a single processor. These models also gained faster SSD speeds, higher base storage, and Wi-Fi 7 and Bluetooth 6 via the N1 chip. Battery life increased slightly across the lineup, while GPU cores now include dedicated Neural Accelerators intended to improve AI workloads.Apple also expanded its display lineup with a new Studio Display XDR model, replacing the Pro Display XDR. The new model offers a 27-inch 5K mini-LED panel with up to a 120Hz refresh rate, HDR brightness up to 2,000 nits, and Thunderbolt 5 connectivity. The standard ‌Studio Display‌ was updated at the same time with two Thunderbolt 5 ports, improved speakers, and a camera that now supports Desk View, but retains its 60Hz panel and 600-nit brightness.All of the newly announced devices became available to pre-order on Wednesday, March 4, with the entire lineup scheduled to launch and begin arriving to customers on Wednesday, March 11.Get the right life insurance for you, for less, and save more than fifty percent at https://www.selectquote.com/macrumors00:00 - Intro01:17 - iPhone 17e06:42 - M4 iPad Air08:46 - M5 MacBook Air11:53 - Sponsor: SelectQuote13:40 - MacBook Pro: M5 Pro and M5 Max Overview21:30 - Studio Display25:58 - Studio Display XDR38:05 - Introducing the MacBook Neo

In this episode of Technovation, Peter High speaks with Stephen Witt, award-winning journalist and author of The Thinking Machine, which has been named Business Book of the Year by Financial Times. Witt writes about Jensen Huang's improbable journey from near-bankruptcy in the 1990s GPU wars to leading NVIDIA at the center of the AI revolution. Witt unpacks how NVIDIA defeated nearly 70 competitors, why Huang began targeting “zero-billion-dollar markets,” and how CUDA became the backbone of modern AI. Key highlights from the episode: How investing in zero-billion-dollar markets created durable platform advantage The emerging bull and bear cases for NVIDIA in robotics, edge computing, and global competition The strategic lessons NVIDIA extracted from surviving a 70-competitor GPU market Why operating with a constant “near-death” mindset shaped long-term execution discipline

Artificial intelligence is rapidly transforming the business landscape, redefining how value is created and where human work fits within the new paradigm. Long-standing advice to amass knowledge and out-execute others is now running up against sophisticated AI agents that can process information and perform tasks at speeds and scales unattainable by humans. In this emerging era, Christopher Lochhead's insights point to a critical shift from being a traditional “knowledge worker” to embracing the future as a “creator capitalist.” On this episode, Christopher Lochhead moves over to the guest chair and answer our questions about AI, Creator Capitalists, and the future of work.  You're listening to Christopher Lochhead: Follow Your Different. We are the real dialogue podcast for people with a different mind. So get your mind in a different place, and hey ho, let's go.   Why the Knowledge Worker Playbook Is Obsolete For decades, success in business hinged on being a master of knowledge and execution. This model rewarded those who reacted effectively, put out fires, and delivered results with established frameworks. However, with AI making information and execution nearly free and instantly accessible, simply reacting and executing is no longer enough. As Christopher Lochhead argues, clinging to this outdated success formula is akin to opening a video rental store in the age of streaming services. Today, the competitive edge lies in moving upstream to activities that AI cannot easily replicate. This means focusing on judgment, unique perspectives, and the ability to define, frame, and solve new problems. Humans cannot out-execute a GPU, but they can out-create one by leveraging skills that remain distinctly human.   The Four Capitals of the Creator Capitalist Framework Lochhead's Creator Capitalist concept rests on the mastery and integration of four kinds of capital: intellectual, relationship, reputational, and financial. Intellectual capital emerges from differentiated insights, deep domain expertise, and unique perspectives. Relationship capital is built through genuine connections and trust within your network, while reputational capital is earned through tangible results and reliability, not just self-promotional branding. Bringing these capitals together creates a flywheel that drives lasting success, even as AI commoditizes old sources of value. Financial capital follows as a natural result of delivering value that others find meaningful. Those able to orchestrate these four capitals will build not just AI-resistant careers but ones supercharged by the new opportunities technology presents.   Unleashing Human Potential: Adapt, Create, and Lead As AI handles more routine tasks, the future belongs to those who cultivate curiosity, creativity, and critical thinking. These human abilities enable us to ask better questions, generate bold ideas, and envision solutions no algorithm can predict. Lochhead urges professionals to take radical responsibility for their careers and continually seek ways to create net new value. Adapting to this shift means letting go of fear and embracing the opportunity to redefine what it means to be valuable. The most successful individuals and organizations will be those who harness AI as a tool to augment their creative power and lead the way into uncharted territory. The age of the creator capitalist has arrived, and it's time to build the future together. To hear more of Christopher Lochhead’s thoughts on Creator Capitalist and the future of work, download and listen to this episode.   Links Want to catch more episode of the AI Agent & Copilot Podcast? You can check them out here: Presented by Cloud Wars | AI Agent and Copilot Podcast | John Siefert LinkedIn | Cloud Wars LinkedIn   We hope you enjoyed this episode of Christopher Lochhead: Follow Your Different™! Christopher loves hearing from his listeners. Feel free to email him, connect on Facebook, X (formerly Twitter), Instagram, and subscribe on Apple Podcast / Spotify!

Apple lo ha vuelto a hacer. Pero esta vez no ha sido un "más de lo mismo con mejor nota". El 3 de marzo de 2026 presentó los chips M5 Pro y M5 Max integrados en los nuevos MacBook Pro, y lo que hay dentro es el cambio de arquitectura más importante desde que llegó el M1. No hablamos de más núcleos ni de un proceso de fabricación más fino. Hablamos de repensar desde cero cómo se construye un chip. En este episodio desmontamos la Fusion Architecture pieza a pieza: qué es un die, por qué dividirlo en dos cambia las reglas del juego, qué implica para la disipación térmica, para la fabricación y para el futuro de Apple Silicon. Hablamos de los Neural Accelerators integrados en cada núcleo GPU, del aumento del ancho de banda del Neural Engine, de los 614 GB/s de memoria del M5 Max y de por qué eso importa más que los GHz cuando hablamos de inteligencia artificial en local. Y hacemos la comparativa con NVIDIA que todo el mundo hace pero casi nadie hace bien: CUDA vs MLX, H100 vs M5 Max, datacenter vs mochila. Sin banderas. Con números reales.

Dive into the heart of the AI revolution with Gary Brode from Deep Knowledge Investing. In this episode, we unravel the complex world of the semiconductors that power AI. Nvidia's GPU dominance to ARM-based innovations, Intel and AMD's CPU roles, and the massive energy demands of data centres. Learn about key deals like Nvidia-Meta's collaboration, investment risks in hyperscalers, and opportunities in nuclear energy and uranium. Perfect for investors navigating the AI boom.

Dive into the heart of the AI revolution with Gary Brode from Deep Knowledge Investing. In this episode, we unravel the complex world of the semiconductors that power AI. Nvidia's GPU dominance to ARM-based innovations, Intel and AMD's CPU roles, and the massive energy demands of data centres. Learn about key deals like Nvidia-Meta's collaboration, investment risks in hyperscalers, and opportunities in nuclear energy and uranium. Perfect for investors navigating the AI boom.

OpenAI's Pentagon Backlash, Microsoft's "MicroSlop" Filter, Apple M5 MacBook Pro Price Hikes, and Washington's Microchip Ban Jim Love covers backlash to OpenAI's rapid Pentagon deal announcement, with Sam Altman admitting it looked opportunistic as ChatGPT uninstall rates and one-star reviews spiked while Anthropic's Claude gained installs; OpenAI then revised contract language to state its AI won't intentionally be used for mass domestic surveillance or by agencies like the NSA without separate approval. He also discusses reports that Microsoft's Copilot Discord filtered the term "MicroSlop," prompting user workarounds and a server lockdown that Microsoft said was an anti-spam measure. Apple's new M5 MacBook Pro lineup adds higher default storage, claims faster internal storage and ~20% GPU gains, but raises prices and introduces a pricier Studio Display XDR with optional nano-texture. Finally, Washington State proposes banning mandatory employee microchip implants amid broader workplace surveillance concerns. 00:00 Sponsor Message Meter 00:19 OpenAI Pentagon Backlash 03:08 Microsoft MicroSlop Filter 05:33 Apple M5 MacBook Prices 07:10 Host Rant On Hype 07:34 Washington Microchip Ban 09:29 Wrap Up And Sponsor

What if the real AI race in 2026 isn't about building bigger models, but about where decisions are made, how fast they happen, and whether they deliver measurable value? In this episode, I'm joined by John Bradshaw, Director of Cloud Computing Technology and Strategy at Akamai, to unpack his predictions for the next phase of cloud, AI inference, and the economics that will shape enterprise technology over the next 12 months. As organizations move beyond experimentation, John explains why the boardroom conversation has shifted from capability to return on investment, and how spiraling compute demands are forcing leaders to rethink the balance between performance, cost, and innovation. We explore why this new financial scrutiny is not slowing AI adoption, but refining it. John shares how inefficient GPU workflows, centralized inference, and poorly aligned architectures are being challenged by a more disciplined approach that pushes intelligence closer to the edge. This shift is not only about latency and performance. It is about building scalable, value-driven platforms that can support real-time decision-making, agentic workloads, and global user experiences without breaking traditional IT budgets. Trust is another major theme throughout our conversation. From the rise of everyday AI agents that quietly handle routine tasks to the growing importance of secure, resilient inference pipelines, John outlines how low-latency edge infrastructure, local processing, and hybrid cloud models will redefine reliability for both enterprises and consumers. We also discuss the smart home backlash following recent outages, and why the next generation of connected products will be designed to work even when the network does not. The episode also looks at the future of streaming, where consolidation, intelligent content delivery, and AI-driven personalization are reshaping both the user experience and the economics behind the platforms. Behind the scenes, orchestration is emerging as a defining capability, with multiple models and services working together to validate outputs, reduce hallucinations, and create more dependable AI systems. This is a conversation about moving from possibility to production, from experimentation to accountability, and from centralized architectures to distributed intelligence. So as AI becomes embedded in every workflow and every customer interaction, will the winners be the companies with the biggest models, or the ones that know exactly where their AI should live, how it should be orchestrated, and how it proves its value every single day?

Pausing a product roadmap for an entire month to point 700 engineers at a single goal is a significant structural shift, but it transformed monday.com. Andrew sits down with VP of R&D Sergei Liakhovetsky to uncover how fixing core infrastructure and adopting a cell-based architecture paved the way for platform scale. Sergei details the exact framework his leadership team used during their 30-day pause to launch user solutions while maintaining a strict zero-bureaucracy policy. The conversation also explores the new realities of reliability as platforms transition from being CPU-bound to heavily GPU-bound under the weight of automated agents.Follow the show:Subscribe to our Substack Follow us on LinkedInSubscribe to our YouTube ChannelLeave us a ReviewFollow the hosts:Follow AndrewFollow BenFollow DanFollow today's guest:monday magic: A tool for generating initial work solutions and boards using simple prompts.monday vibe: An app builder that allows users to create custom applications on top of the monday.com platform.Sidekick: The horizontal AI assistant/copilot that works across the entire platform to help with tasks like data management and content generation.Agent Factory: A platform for building vertical, specialized agents that can handle specific workflows and roles.Connect with Sergei Liakhovetsky on LinkedInOFFERS Start Free Trial: Get started with LinearB's AI productivity platform for free. Book a Demo: Learn how you can ship faster, improve DevEx, and lead with confidence in the AI era. LEARN ABOUT LINEARB AI Code Reviews: Automate reviews to catch bugs, security risks, and performance issues before they hit production. AI & Productivity Insights: Go beyond DORA with AI-powered recommendations and dashboards to measure and improve performance. AI-Powered Workflow Automations: Use AI-generated PR descriptions, smart routing, and other automations to reduce developer toil. MCP Server: Interact with your engineering data using natural language to build custom reports and get answers on the fly.

What happens when the cost of intelligence drops to zero? The only thing that matters is knowing how to give the right instructions.In this episode, Andreas Bachmann – co-founder of Adacor, a managed cloud and critical infrastructure provider serving banks, automotive, healthcare, and energy clients across Germany – shares what 22 years of deliberate, founder-led growth actually looks like. We explore the real tension between innovation and zero-tolerance uptime, the co-founder crisis that almost broke the company, and why Andreas believes the primary job of every knowledge worker in five years won't be doing the work – it'll be managing the agents doing it for them.What You'll Discover:[00:01:19] Innovating When Failure Is Not an Option → How Adacor runs experiments for critical infrastructure clients who can't afford a single hiccup – and the mental model that makes it work[00:05:30] The Sustainable Growth Playbook → Why Andreas chose deliberate, step-by-step growth over hypergrowth – and how that decision made Adacor more competitive, not less[00:13:49] The Co-Founder Crisis Nobody Talks About → At 40–50 people, Adacor fractured into silos and the founding team needed “marriage counseling” – what they decided, and who stepped back[00:17:34] Self-Organization Without Chaos → How Adacor implemented OKRs, dailies, and retrospectives in a high-stakes environment – and the one thing that makes retros actually stick[00:23:37] Building a Human-Centered Tech Company → From family compatibility programs to volunteer firefighter support – why Andreas treats the company as the strong one, not the individual[00:27:26] The AI Question: Bullshit or Real? → Why Andreas went all-in on AI in 2022, how Adacor hacked EU innovation grants to build an AI team years early, and why he skipped the GPU commodity race entirely[00:34:16] The Future of Work Is Managing Agents → Andreas's thesis on what happens when intelligence is automated and essentially free – and what human value actually looks like on the other sideKey Takeaways:Sustainable growth is a competitive advantage in high-trust industries – adding people too fast breaks the thing clients pay you for“Fast fashion software”: non-developers are already using AI to write and discard code; this is a glimpse of where all knowledge work is headedThe best retros are useless without a committed “what do we do about it now?” – every retrospective at ATCO must produce 1–3 actionable initiativesThe co-founder transition from parallel silos to one clear direction is one of the most underreported breaking points in company buildingThe new leadership superpower isn't having all the answers – it's knowing when to step back and trust the people who doAbout Andreas Bachmann:Andreas is co-founder and CEO of Adacor, a German managed cloud and critical infrastructure company he's been building for over 22 years with a deliberate focus on stability, human-centered culture, and innovation that doesn't break things. He's also a founding force behind Media Monster, an initiative supporting mental health and work-family compatibility in tech.

QuitGPT Claims Surge, NVIDIA's Vera Rubin 10x Efficiency, Remote Work Pay Premium & Brain Cells Play Doom | Hashtag Trending Jim Love covers claims from QuitGPT.org that 1.5 million people have taken action against ChatGPT, noting the figure mixes signups, shares, and cancellations and that substantiated numbers remain unclear amid negative OpenAI headlines and a possible rise in interest in Anthropic's Claude, which hit #1 on the Apple App Store and saw an outage from "unprecedented demand." NVIDIA announces its next AI platform, Vera Rubin, claiming 10x performance per watt over Grace Blackwell, higher NVLink bandwidth, and a rack-scale 72-GPU/36-CPU system aimed at lowering energy per inference and defending market leadership. A French study finds remote/hybrid workers earn about 12% more (about 6% after controls). Researchers also taught lab-grown human neurons on a chip to play Doom via electrical feedback. Apple updates iPad Air with the M4 chip, and a developer describes being locked out of a premium Google AI account with no clear human support escalation. 00:00 Sponsor Message 00:21 Today's Headlines 01:00 QuitGPT Backlash 04:28 Nvidia Vera Rubin 07:12 Remote Work Pay Premium 09:13 Brain Cells Play Doom 11:02 M4 iPad Air Update 11:35 Locked Out of AI Account 13:25 Wrap Up and Sponsor Thanks

Dan Nathan and Guy Adami cover PPI, upcoming earnings, and this week's jobs report. They focus on mounting stress in the AI infrastructure and financing complex: CoreWeave's post-earnings drop, heavy customer concentration, funding challenges, and Jim Chanos' critique that its GPU-leasing model loses money and shows distress-level liquidity, alongside declines in Apollo, KKR, Blackstone, and banks. They contrast Nvidia's strong quarter and 60% growth outlook with stock stagnation, discuss Broadcom as a key AI barometer, and note ongoing software multiple and margin compression highlighted by volatile moves in Workday and Salesforce. Despite rising VIX swings, falling 10-year yields, and consumer-credit concerns signaled by AmEx, Capital One, Klarna, and Walmart trade-down commentary, the S&P remains near highs; they also discuss crude's rebound amid Middle East tensions and Bitcoin weakness pressuring MicroStrategy. After the break, Jen & Kristen join Dan and Guy live from the iConnections Global Alts conference in Miami to unpack an “AI panic” market day, why higher productivity could mean higher rates, and what private credit hiccups really signal for hedge funds and alts. They also explain how The Wall Street Skinny is turning arcane finance jargon into plain English for everyone from college students to the C‑suite, plus why there are no dumb questions when it comes to bonds, credit, and careers on Wall Street. Timecodes 0:00 - Intro 2:00 - CoreWeave & The Software Slide 17:30 - VIX, SPX & The Consumer 25:00 - Yields & Crude 28:30 - Bitcoin & Broader Market 33:20 - He Said, She Said

I am thrilled to welcome Marenza Altieri Douglas, an executive in sales and technology.  She's trained in structured enterprise environments, start ups, and is steeped in opening new markets and building commercial enterprise.  That's not going to be our focus today, instead we talk about how she is an incredible storyteller, rooted in concepts like disruption and cultivation.  Her personal story is key to the narrative, and I was thrilled she is joining us to share that story and how she ties it all together, leading and operating in the current business climate.  Marenza Altieri Douglas' career sits at the intersection of technology evangelism and disciplined execution. Trained in structured, enterprise environments and refined in startups and scale-ups, she specializes in defining strategic direction, opening new markets, and building compelling commercial propositions for enterprise and C-suite customers across Fortune 500 and Global 5000 organizations.  She has worked across and alongside technologies including Conversational and Generative AI, APIs, DevOps, open-source platforms, cloud and containerized architectures, enterprise mobility, security, communications, media and broadcast, telecoms, and digital platforms. AI is a natural evolution of this journey, alongside a strong strategic interest in GPU-enabled infrastructure and quantum technologies.  Marenza is known for building high-trust relationships, spotting and growing talent, and connecting product, engineering, and commercial teams around clear outcomes. A natural storyteller and facilitator, I enjoy shaping narratives that help organizations and customers understand why a technology matters, not just what it does.(4:50) We delve into Marenza's formative years that put her on her current path. She shares her personal and professional story.  (17:18) When did Marenza realized that “disruption” and challenging things become a part of her brand?  (22:38) What does Marenza feel are some of the important qualities that people should embody?  (28:20) Marenza shares how she focuses on the future and the next generation.  (39:16) We reflect on what Marenza would like her impact to be over the next couple of years.Connect with Marenza Altieri-Douglashttps://www.linkedin.com/in/marenza/    Subscribe: Warriors At Work PodcastsWebsite: https://jeaniecoomber.comFacebook: https://www.facebook.com/groups/986666321719033/Instagram: https://www.instagram.com/jeanie_coomber/Twitter: https://twitter.com/jeanie_coomberLinkedIn: https://www.linkedin.com/in/jeanie-coomber-90973b4/YouTube: https://www.youtube.com/channel/UCbMZ2HyNNyPoeCSqKClBC_w

This week on The GovNavigators Show, Robert Shea and Adam Hughes sit down with Deep Grewal, Vice President of Public Sector at MinIO, to unpack the findings of a new survey on the federal government's AI readiness, and why so many agencies are still stuck in the pilot phase.While AI ambition is everywhere, Deep explains that the real bottleneck is in data management. From lineage and governance to infrastructure, portability, and total cost of ownership, the conversation makes the case that the unglamorous foundational work will determine which agencies actually scale AI and which remain in perpetual experimentation.They dig into the tension between cloud-first and cloud-smart, the rise of hybrid and sovereign architectures, the GPU and storage crunch, and why AI must become a mission-wide capability rather than a bolt-on “innovation project.” Deep also lays out a practical checklist for moving to enterprise AI: get your data house in order, modernize infrastructure, upskill the workforce, establish governance, and prove the ROI.If you're trying to move from AI pilots to real production, this episode is your roadmap. Show Notes:MinIO's Federal AI Readiness GapAnthropic's stand-offOne man's big bet against DOGEWhat's on the GovNavigators' Radar:Mar 4, 2026Alliance for Digital Innovation's Understanding OneGov: Discussions with GSA LeadershipMar 5, 2026The MUST ATTEND Driving Government Efficiency SummitMar 11, 2026 Data Foundation event on Treasury's Do Not PayMar 19, 2026 RSM Webinar: AI Governance and Responsible Adoption in Government 

AI funding rounds are getting bigger. Infrastructure bets are getting steeper. And the SaaS model is back under pressure. On episode 294 of The Six Five Pod, Patrick Moorhead and Daniel Newman break down the $110B OpenAI raise, Amazon's expanded role, AMD's $100B Meta deal, sovereign cloud momentum, and whether or not the SaaS premium is being permanently eroded. The handpicked topics for this week are: OpenAI's $110B Funding Round & Amazon's $50B Commitment: OpenAI secured a $110B round backed by Amazon, NVIDIA, and SoftBank. Amazon committed $50B over eight years, including Tranium capacity, co-development, Bedrock integration, and custom model initiatives. Microsoft remains the exclusive API cloud provider, but the competitive cloud dynamics are shifting. Anthropic, the Pentagon & the AI Safety Line: Anthropic risks a $200M DoD contract over refusing to drop safety restrictions related to mass surveillance and automated weapons. Pat and Dan explore the ethics and competitive positioning of this, and what happens if another lab steps in. Model Distillation & IP Risk: Anthropic cited 24,000 fraudulent accounts generating 16 million interactions to distill model capabilities. The episode examines IP theft, enforcement gaps, and global competition. DeepSeek & NVIDIA Blackwell Reports: Recent reports suggest DeepSeek leveraged NVIDIA Blackwell chips. The hosts discuss export controls, enforcement realities, and whether this was ever realistically in doubt. Microsoft Sovereign Cloud Goes GA: Microsoft introduced full-stack Azure sovereign cloud capabilities with support for disconnected operations. Sovereignty, regulatory compliance, and latency management are becoming core enterprise and government requirements. AMD's $100B Meta AI Infrastructure Deal: AMD secured a massive multi-gigawatt inference-focused deal with Meta using MI450. The discussion centers on competitive dynamics with NVIDIA, scale-up architecture, and whether AMD can materially shift market share. Intel & SambaNova Alignment: Intel Capital invested in SambaNova's Series E. The hosts examine inference strategy, CPU resurgence, and how Intel rounds out its AI positioning while advancing its GPU roadmap. The Flip: Is SaaS Permanently Repriced? Are enterprise SaaS multiples structurally resetting due to AI agents and consumption models, or is the market misreading enterprise AI adoption speed? Nuance emerges around consolidation, consumption pricing, and the durability of complex enterprise platforms. Bulls & Bears: NVIDIA, Salesforce, Synopsys, Dell, Snowflake, IBM, Everpure, HP Strong earnings across several big tech companies met with mixed market reactions. Terminal value concerns, consumption transitions, stock-based compensation, and memory constraints shape sentiment more than raw performance. For a deeper dive into each topic, subscribe to The Six Five Pod so you never miss an episode.

Gaming can of course be an addiction. Like all things in life, balance is important and we ourselves are not above playing the occasional computer game as we all need a little escape here and there. In spirts and not letting it take over our life of course. Amongst other more creative things, one of our rituals we do for fun with our daughter is watch anime or game together. And recently we did an extremely rare acquisition and bought a new but used off Craigslist graphics card. By the way, we highly recommend the book "Chip War" on the subject of microchips and the massive importance of two companies on the planet, one of which is called ASML (Advanced Semiconductor Materials) which does extreme ultraviolet photolithography, creating the world's only machines that are required to manufacture the most advanced microchips, and the other is called TSMC (Taiwan Semiconductor Manufacturing Company) and is the company that manufactures the majority of the world's microchips using ASML hardware in Taiwan and is why that is such a crucial small country in the Earth realm currently. Right around the time of this new GPU acquisition a new game had recently been released which caught our eye and was an excuse to try out the new hardware. ———An Infinite Path podcast official URL http://www.aninfinitepath.comSpotify | iTunes | YouTube | Overcast FM | Stitcher | Player FM

On the latest Blockspace roundup, the gang cover's Block's 40% workforce reduction and our scoop that Magic Eden is quitting the Bitcoin and Ethereum NFT game. Get your tickets to OPNEXT 2026 before prices increase! Join us on April 16 in NYC for technical discussions, investor talks, and intimate conversation with the brightest minds in Bitcoin. Welcome back to The Blockspace Podcast! Today, Charlie and Colin cover the Block's 40% workforce reduction and why the stock ripped 20% on the news. We also dive into the bitcoin mining conditions that are driving hashprice to all-time lows, Blockspace's scoop that Magic Eden is sunsetting its Bitcoin Ordinals marketplace, MARA's latest AI partnership, and the Terra/Luna lawsuit against Jane Street. Plus, Luxor's Michael San Miguel joins the show to discuss the ins and outs of the GPU market.  Subscribe to the newsletter! https://newsletter.blockspacemedia.com Notes: * Block laid off 40% of its 10,000 employees. * Block stock surged 20% after the layoff news. * Bitcoin hash price hit an all-time low of $28. * Bitcoin difficulty adjusted upward by 14.73%. * Magic Eden is shutting down BTC and ETH marketplaces, multi-chain wallet * Bitdeer sold all its bitcoin; Cipher plans to sell its bitcoin in 2026 * MARA forms partnership with data center developer Starwood Timestamps: 00:00 Start 03:33 Hashrate update via Luxor's Hashrate Index 09:29 Block lays off 40% of staff 16:37 Magic Eden shutting down 25:54 GPUs & compute 28:03 GPU vs ASIC complexity 29:04 Upgrading hardware 32:16 Finding a compute buyer 34:00 Powershell vs Neocloud 37:12 Compute still in price discovery mode 42:05 MARA earnings 45:20 CIPHER dumping bags 48:44 Jane Street is the new boogyman 59:34 Everyone's short MSTR

A Note from James:In the last episode, we talked about whether Martin Shkreli really deserves the label “most hated man in America.” My conclusion was no, and I hope you came to the same conclusion after hearing his perspective.In this episode, we shift gears completely. We talk about Bitcoin, crypto, AI, energy, optical computing, and what the future of technology might actually look like.Martin has a very unusual combination of skills—finance, biotech, programming—and I always enjoy hearing how he connects ideas across different fields. That's what this conversation is about.Episode Description:What happens when AI demand collides with the limits of computing power and energy?In Part 2, Martin Shkreli and James explore the future of technology—from crypto vulnerabilities to optical computing, GPU scaling, and the potential energy crisis driven by artificial intelligence.They discuss whether Bitcoin can survive quantum computing, why stablecoins solve real-world financial problems, and how computing architecture may shift beyond traditional silicon chips. The conversation then moves into AI economics: why companies might spend billions on compute to make better decisions, how energy constraints could shape innovation, and why optical computing could become the next major breakthrough.This episode isn't about controversy—it's about technological leverage, incentives, and where computation is heading next.What You'll Learn:Why quantum computing could eventually threaten Bitcoin's encryptionThe real-world advantages of stablecoins and decentralized paymentsHow AI demand could create massive new energy constraintsWhy optical (photonic) computing may outperform traditional silicon chipsHow businesses might use large-scale AI compute for strategic decisionsTimestamped Chapters:[00:02:00] Bitcoin, Encryption & Quantum Computing Risks[00:03:02] A Note from James[00:03:34] Crypto Markets: Speculation vs. Utility[00:05:23] Banking Control, Debanking & Stablecoins[00:07:40] Moore's Law, Huang's Law & The Limits of Silicon[00:08:45] Optical Computing Explained[00:09:12] NVIDIA, Parallelization & Power Consumption[00:10:24] Energy Constraints & The Electrical Grid[00:11:41] AI Energy Demand vs. Countries[00:12:24] Corporate AI Decision-Making at Scale[00:13:37] The Coming Explosion of AI Compute[00:14:20] Energy Efficiency vs. Speed[00:15:17] GPU Efficiency Improvements & Jevons Paradox[00:17:00] Why AI Is Different from Traditional Computing[00:17:47] Optical vs. Quantum vs. DNA Computing[00:18:19] Why Optical Computing Fits AI Perfectly[00:19:28] Precision, Bits & Neural Networks[00:21:24] Error Tolerance in AI Systems[00:22:00] Fiber Optics & Existing Infrastructure[00:23:16] New Computing Paradigms Beyond Silicon[00:24:00] Matrix Multiplication & AI Workloads[00:24:53] Closing ThoughtsSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

AI is not just getting smarter. It is getting faster by learning how to optimize the hardware it runs on. In this episode, Sharon Zhou, VP of AI at AMD and former Stanford AI researcher, explains how language models are beginning to write and optimize their own GPU kernel code. We explore what self improving AI actually means, how reinforcement learning is used in post training, and why kernel optimization could be one of the most overlooked scaling levers in modern AI. Sharon breaks down how GPU efficiency impacts the cost of training and inference, why catastrophic forgetting remains a challenge in continual learning, and how verifiable rewards from hardware profiling can help models improve themselves. The conversation also dives into compute economics, synthetic data, RLHF, and why infrastructure may define the next phase of AI progress. If you want to understand where AI scaling is really happening beyond bigger models and more data, this episode goes under the hood. Stay Updated: Craig Smith on X: https://x.com/craigss Eye on A.I. on X: https://x.com/EyeOn_AI (00:00) Preview and Intro (00:25) Sharon Zhou's Background and Transition to AMD (02:00) What Is Self-Improving AI? (04:16) What Is a GPU Kernel and Why It Matters (07:01) Using AI Agents and Evolutionary Strategies to Write Kernels (11:31) Just-In-Time Optimization and Continual Learning (13:59) Self-Improving AI at the Infrastructure Layer (16:15) Synthetic Data and Models Generating Their Own Training Data (20:48) AMD's AI Strategy: Research Meets Product (23:22) Inside the NeurIPS Tutorial on AI-Generated Kernels (30:59) Reinforcement Learning Beyond RLHF (39:09) 10x Faster Kernels vs 10x More Compute (41:50) Will Efficiency Reduce Chip Demand? (42:18) Beyond Language Models: Diffusion, JEPA, and Robotics (45:34) Educating the Next Generation of AI Builders

Thu, 26 Feb 2026 21:45:00 GMT http://relay.fm/connected/592 http://relay.fm/connected/592 The Rickies (March 2026) 592 Federico Viticci, Stephen Hackett, and Myke Hurley Apple is hosting a mysterious media experience next week, and in anticipation of new products, Stephen, Myke, and Federico make predictions about what is coming. Apple is hosting a mysterious media experience next week, and in anticipation of new products, Stephen, Myke, and Federico make predictions about what is coming. clean 3983 Subtitle: Lil' ChippyApple is hosting a mysterious media experience next week, and in anticipation of new products, Stephen, Myke, and Federico make predictions about what is coming. This episode of Connected is sponsored by: Insta360: Introducing the Insta360 Wave and the Link 2 Pro. Sentry: Mobile crash reporting and app monitoring. New users get $100 in Sentry credits with code connected26. Squarespace: Save 10% off your first purchase of a website or domain using code CONNECTED. Links and Show Notes: Get Connected Pro: Preshow, postshow, no ads. Submit Feedback Apple in 2025: The Six Colors report card – Six Colors Six Colors' Apple in 2025 Report Card - MacStories My Full Responses for the 2025 Six Colors Report Card - 512 Pixels Upgrade #604: The Shifting Sands of Liquid Glass - Relay Samsung Galaxy S26/Ultra Impressions: 1 Crazy Display Feature! - MKBHD - YouTube Samsung Galaxy Unpacked 2026 in 12 minutes - The Verge - YouTube Introducing Perplexity Computer 2026 March Keynote Rickies – Rickies.net Keynote Rickies, March 2026 – Rickies.co Wood Blocks | Nintendo The MacBook Air's wedge is truly gone — and I miss it already | The Verge Leaker Says Apple's Lower-Cost MacBook Will Have These 8 Limitations - MacRumors M5 Pro chip could separate CPU and GPU in 'server grade' chips - 9to5Mac 2.5D integrated circuit - Wikipedia Apple Reportedly Agrees to 100% Price Hike on Samsung Memory Chips - MacRumors New ‘F1: Drive to Survive' season is coming to Apple TV - 9to5Mac Apple TV reveals new space-race thriller series is coming soon - 9to5Mac

BrainChip CEO Sean Hehir joins me to unpack where artificial intelligence is actually headed—and why the dominant “everything in the data center” narrative is incomplete.Most AI conversations fixate on massive models, GPU farms, and trillion-dollar infrastructure bets. This episode shifts the frame. Sean and I explore the structural reality that power consumption, latency, and grid constraints are forcing AI to decentralize—and what that means for founders, engineers, and the broader economy.Sean explains how neuromorphic computing and ultra-low-power silicon enable AI inference outside the data center—inside wearables, medical devices, drones, manufacturing systems, and even space applications. We examine why CPUs and GPUs aren't optimized for edge workloads, how custom silicon changes the economics, and why power efficiency isn't a side issue—it's the bottleneck that determines what scales.The conversation expands into workforce displacement, labor fluidity, productivity cycles, and whether technological acceleration inevitably creates unemployment crises—or simply reshuffles value creation again, as history repeatedly shows.This isn't a speculative futurism episode. It's a grounded look at model trends, infrastructure limits, and how companies survive inside a market moving at month-scale rather than decade-scale.The lesson isn't that AI replaces everything.It's that architecture determines outcomes.TL;DR* AI is centralizing in data centers—but it's also rapidly decentralizing to the edge* Power constraints will shape the next phase of AI more than hype cycles* Neuromorphic and event-driven silicon drastically reduce energy per compute* Edge AI enables medical wearables, safety detection, space systems, and industrial automation* Models are getting larger—but optimization techniques will shrink them into smaller form factors* Productivity gains historically displace tasks—not human adaptability* The future isn't about bigger servers—it's about smarter distribution* Lowest power per compute is a strategic advantage, not a marketing lineMemorable Lines* “Don't bet against humanity. We're very creative.”* “The future of AI isn't just in data centers.”* “Power isn't a feature—it's the constraint.”* “If you're the lowest power solution, you will always have customers.”* “Architecture decides what becomes possible.”GuestSean Hehir — CEO of BrainChipTechnology executive leading the commercialization of neuromorphic AI processors focused on ultra-low-power edge inference. Oversees BrainChip's evolution from early engineering innovation to market-driven, customer-focused deployment.

By the end of 2026, AI capital expenditure is projected to hit nearly $700 billion. The question isn't who has the best model, but who has the most creative financing to build out AI infrastructure and beyond. Sarah Guo is joined by Neil Tiwari, Managing Director at Magnetar Capital, a financial innovator helping the AI industry scale from billions to trillions of dollars in CapEx. Neil explains some of the debt structures used to finance massive GPU clusters, who is taking the risk, and how the industry is maturing. Sarah and Neil also discuss how power distribution, energy storage, and physical materials like steel are the bottlenecks of the AI industry. Plus, Neil gives his take on the future of inference-optimized clouds, and why the market shift away from software and into infrastructure might be an overreaction. Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil  Chapters: 00:00 – Cold Open 00:05 – Neil Tiwari Introduction 00:26 – Magnetar's Story 01:28 – Why CoreWeave Helped Magnetar Win 06:15 – Scaling CapEx Efficiently 09:02 – Debunking GPU Collateral Risk 11:42 – How Deal Structures Evolve 13:01 – What Bottlenecks Buildout 15:28 – Circular Financing Critiques 17:35 – The Shift from Training to Inference Workloads 23:10 – AI Factories 24:12 – Constraints of the Current Power Grid 28:27 – Sovereign Compute Buildouts 29:54 – Physical AI Capital Needs 32:48 – The Capital Rotation Away from SaaS 36:04 – Conclusion

Thu, 26 Feb 2026 21:45:00 GMT http://relay.fm/connected/592 http://relay.fm/connected/592 Federico Viticci, Stephen Hackett, and Myke Hurley Apple is hosting a mysterious media experience next week, and in anticipation of new products, Stephen, Myke, and Federico make predictions about what is coming. Apple is hosting a mysterious media experience next week, and in anticipation of new products, Stephen, Myke, and Federico make predictions about what is coming. clean 3983 Subtitle: Lil' ChippyApple is hosting a mysterious media experience next week, and in anticipation of new products, Stephen, Myke, and Federico make predictions about what is coming. This episode of Connected is sponsored by: Insta360: Introducing the Insta360 Wave and the Link 2 Pro. Sentry: Mobile crash reporting and app monitoring. New users get $100 in Sentry credits with code connected26. Squarespace: Save 10% off your first purchase of a website or domain using code CONNECTED. Links and Show Notes: Get Connected Pro: Preshow, postshow, no ads. Submit Feedback Apple in 2025: The Six Colors report card – Six Colors Six Colors' Apple in 2025 Report Card - MacStories My Full Responses for the 2025 Six Colors Report Card - 512 Pixels Upgrade #604: The Shifting Sands of Liquid Glass - Relay Samsung Galaxy S26/Ultra Impressions: 1 Crazy Display Feature! - MKBHD - YouTube Samsung Galaxy Unpacked 2026 in 12 minutes - The Verge - YouTube Introducing Perplexity Computer 2026 March Keynote Rickies – Rickies.net Keynote Rickies, March 2026 – Rickies.co Wood Blocks | Nintendo The MacBook Air's wedge is truly gone — and I miss it already | The Verge Leaker Says Apple's Lower-Cost MacBook Will Have These 8 Limitations - MacRumors M5 Pro chip could separate CPU and GPU in 'server grade' chips - 9to5Mac 2.5D integrated circuit - Wikipedia Apple Reportedly Agrees to 100% Price Hike on Samsung Memory Chips - MacRumors New ‘F1: Drive to Survive' season is coming to Apple TV - 9to5Mac Apple TV reveals new space-race thriller series is coming soon - 9to5Mac

DOD – Disrupter Disrupters China markets reopening after Lunar New Year Mexico Cartel Wars Refunds requested for the illegal tariffs PLUS we are now on Spotify and Amazon Music/Podcasts! Click HERE for Show Notes and Links DHUnplugged is now streaming live - with listener chat. Click on link on the right sidebar. Love the Show? Then how about a Donation? Follow John C. Dvorak on Twitter Follow Andrew Horowitz on Twitter Warm-Up - The CTP for Caterpillar announced - DOD - Disrupter Disrupters - China markets reopening after Lunar New Year - Mexico Cartel Wars (Jalisco) Markets - Mortgage Rates - looking good! - Tariffs found illegal - that is not stopping anything - Refunds requested for the illegal tariffs - Monday's big drop and AI taking a bite out of stock prices Tariffs - First, who actually knows what is going on. 100% chaos - Supreme court ruled illegal (6-3) - 10% flat across all countries immediately added - Wait a day and make that 15% - FedEx seeks refund for illegal IEEPA tariffs imposed by Trump after the Supreme Court ruled Trump's tariffs exceeded authority - Numerous lawsuits expected for IEEPA tariff refunds - Apple has spent more than $3 billion on tariffs since President Donald Trump enacted his trade policies. What about that? (HOW TO FIGURE OUT WHO GETS THE REFUND) --- Estimate that $175B tariffs have been collected alreay - A group of 22 U.S. Senate Democrats on Monday introduced legislation that would require President Donald Trump's administration to fully refund within 180 days all of the revenue, with interest, collected from tariffs struck down by the U.S. Supreme Court. - The legislation would require the Customs and Border Protection agency, which collects tariffs at U.S. ports of entry, to prioritize small businesses. - The U.S. Customs and Border Protection agency said it will halt collections of tariffs imposed under the International Emergency Economic Powers Act at 12:01 a.m. EST (0501 GMT) on Tuesday Stop The Presses - After years of JCD's rants....... - Apple will soon introduce MacBooks with touch screens - Apple Inc.'s initial touch Macs will have the Dynamic Island at the center top of the display and OLED screen technology. The new MacBook Pro models will have a refreshed, dynamic user interface that can shift between being optimized for touch or point-and-click input. Europe Reacts - "The current situation is not conducive to delivering 'fair, balanced, and mutually beneficial' transatlantic trade and investment, as agreed to by both sides" in the joint statement setting out the terms of last year's trade agreement, the Commission said. "A deal is a deal." - All active discussions are halted on any USA/Europe trade deal The Potential Winners - Brazil and China may be the winners here - Chinese President Xi Jinping has a boost in bargaining power after the US Supreme Court invalidated Donald Trump's broad emergency tariffs, a key point of leverage over China. - The removal of tariff threats will make it harder for Trump to press Xi for larger purchases of certain products and leaves him without a key weapon to strike back if Chinese negotiators make fresh demands. - Xi's team will likely push harder for access to advanced semiconductors, the removal of trade restrictions on Chinese companies, and reduced US support for self-ruled Taiwan, according to Wu Xinbo, director at Fudan University's Center for American Studies. NVDA Earnings - NVIDIA drops its fiscal Q4 2026 (ended Jan 2025) results tomorrow—another make-or-break moment for the AI trade. - The bar is sky-high after years of blowout beats, but whispers of "peak AI" and slowing growth momentum have investors on edge. --- Consensus Expectations : ----Revenue: ~$65.6–$66.1 billion (up ~67–68% YoY from last year's ~$39B; guided $65B ±2% in prior report) ------EPS (adjusted/non-GAAP): ~$1.50–$1.53 (up ~70–72% YoY from $0.89). --------Gross margins: Targeting ~75% non-GAAP (holding strong despite supply chain noise). -----------Key driver: Data Center segment expected to crush ~$58–$60B, fueled by Blackwell ramp and hyperscaler spend. Home Depot Earnings - The home-improvement retailer gained 2.7% after posting fourth-quarter adjusted earnings of $2.72 per share on revenues of $38.20 billion. - That exceeded the per-share earnings of $2.54 on revenues of $38.12 billion expected by analysts polled by LSEG. AMD News - The semiconductor maker rose about 11% after it inked a multiyear deal with Meta to lend up to 6 gigawatts of its graphics processing units to artificial intelligence data centers. - The cost of the deal is unclear, but the companies' agreement includes a a performance-based warrant that could amount to up to 160 million of AMD shares, according to a statement dated Tuesday. - Meta has committed to deploying up to 6 gigawatts (GW) of AMD's Instinct GPUs (high-end graphics processing units optimized for AI workloads) to power its massive AI data centers. - Analysts estimate the GPU portion alone could be worth $60–$100+ billion over 5+ years Mortgage Rates - The average rate on the popular 30-year fixed mortgage fell to 5.99% on Monday, according to Mortgage News Daily, matching its lowest levels since 2022. - Last year at this time the rate was 6.89%. - A buyer putting 20% down on the median priced home, about $400,000 according to the National Association of Realtors, would have a monthly payment of $1,916 for the principal and interest. One year ago, that payment would have been $2,105, a difference of $189. Life Insurance Record - Manulife Financial Corp. sold a $300 million life insurance policy in Singapore, topping what Guinness World Records certified as the most valuable policy ever issued. - The policy surpasses the previous record of $250 million, set by HSBC Life in Hong Kong in 2024. Manulife said in a statement Tuesday that the deal reflects growing demand from ultra-wealthy clients to preserve their assets. - In Singapore over the past 12 months, Manulife has issued 25 individual policies each worth more than $50 million. Bitcoin Rout - Gemini said it was axing as much as a quarter of its staff and exiting the UK, European Union and Australia entirely. - This week, it parted with its chief operating officer, chief financial officer and chief legal officer, all in a single day. - Its stock has fallen more than 80% from a post-listing high last year, collapsing its market value from a peak of almost $4 billion to under $700 million. Over the Greenland - USA sending a "hospital ship" over - Trump's post on the ship came hours after Denmark's Joint Arctic Command said it had evacuated a crew member who required urgent medical treatment from a U.S. submarine in Greenlandic waters, seven nautical miles outside of Greenland's capital, Nuuk. - Greenland said thanks but no thanks So Long! - U.S. investors are pulling money out of their own stock market at the fastest pace in at least 16 years as Big Tech returns fade and better-performing overseas markets look more attractive. - In the last six months, U.S.-domiciled investors have pulled some $75 billion from U.S. equity products, with $52 billion flowing out since the start of 2026 alone, the most in the first eight weeks of the year since at least 2010 AI Disruption - DOD (Disruption of Disrupters) - CrowdStrike -9.8% and other cybersecurity names under heavy pressure again as AI disruption fears build following Anthropic's Claude Code release - - Cybersecurity stocks are under broad pressure today, extending recent weakness following Friday's launch of Claude Code Security by Anthropic. Claude Code Security scans codebases for vulnerabilities and suggests software patches for human review, fueling a narrative that AI platforms may be moving more quickly into parts of the security workflow than investors had previously expected. For cybersecurity, that raises concern around the forward demand outlook and competitive positioning, particularly in areas tied to application security, cloud security, identity workflows, and security operations automation, where AI-native tools could start to narrow perceived differentiation. - The move suggests investors are still sorting through the implications for product overlap, pricing power, and competitive positioning as AI capabilities evolve quickly. - IBM shares dropping toward lows of the session; attributed to news that Claude can automate cobol modernization COBOL (Common Business-Oriented Language) is a high-level, English-like programming language created in 1959 for business, finance, and administrative data processing. It is renowned for its verbosity, readability, and reliability, processing massive amounts of transactions on mainframe systems,, notes NetCom Learning and IBM. Despite being decades old, it remains critical in banking, insurance, and government sectors. - It is estimated that 70-80% of the world's business transactions are processed by COBOL Grok's Prediction about Future of OpenAi/ChatGPT Scenario Likelihood (My Estimate) Key Factors Outcome for OpenAI/ChatGPT Thriving Leader Medium (40%) Sustained breakthroughs, partnerships (e.g., Microsoft), regulatory wins OpenAI as AI giant; ChatGPT as ecosystem hub for agents/robots Evolved Survivor High (50%) Adaptation to agents/hardware; mergers Exists but rebranded; ChatGPT integrated into daily life tools Decline/Acquisition Low (10%) Overcompetition, funding collapse Absorbed or legacy; ChatGPT commoditized or obsolete Quick check on Europe Shares - European company earnings growth is picking up this reporting season against a tentatively improving economic backdrop, but wary investors are demanding more than solid results to justify sky-high valuations. - Companies representing 57% of Europe's market capitalization have reported so far, achieving average earnings growth of 3.9% in the fourth quarter, ahead of estimates for a final result of a contraction of 1.1% --- That is a big differential.... +3.9 vs -1.1 Iran Talks - News over the weekend that Iran will look to discuss a variety of items and potentially get a deal.... energy, mining and aircraft - Best guess: Iran will string us along like Russia is doing and we will say we have some kind of bogus deal. --- There is some talk of US "going in" as we are building military presence. Supposedly there are some saying it could be a multi-week incursion. - What is the plan - Regime change? What is this? - A divided Supreme Court on Tuesday ruled that Americans can't sue the U.S. Postal Service, even when employees deliberately refuse to deliver mail. - By a 5-4 vote, the justices ruled against a Texas landlord, Lebene Konan, who alleges her mail was intentionally withheld for two years. Konan, who is Black, claims racial prejudice played a role in postal employees' actions. - Justice Clarence Thomas, writing for a majority of five conservative justices, said the federal law that generally shields the Postal Service from lawsuits over missing, lost and undelivered mail includes “the intentional nondelivery of mail.” - So can ballots just be thrown in garbage for mail-ins for one party that will throw out another party's?     Love the Show? Then how about a Donation? HE CLOSEST TO THE PIN for CATERPILLAR Winners will be getting great stuff like the new "OFFICIAL" DHUnplugged Shirt!     FED AND CRYPTO LIMERICKS   See this week's stock picks HERE Follow John C. Dvorak on Twitter Follow Andrew Horowitz on Twitter

U svetu softvera, grešku rešavate jednostavnim patch-om. Ali kada razvijate hardver, svaka greška koju pronađete pre proizvodnje je besplatna, dok ona koju otkrijete tek na gotovom čipu košta milione dolara i mesece bačenog vremena. Kako izgleda raditi u industriji gde pravo na grešku praktično ne postoji? U trećoj epizodi Pojačalo specijala Next Silicon, Ivan razgovara sa Vladimirom Miloševićem, liderom tima za verifikaciju hardvera u ovoj kompaniji. Kroz razgovor otkrivamo fascinantan i kompleksan svet razvoja čipova - od početne ideje i arhitekture, preko rigoroznog testiranja pre proizvodnje, pa sve do finalne fizičke realizacije. Vladimir objašnjava zašto je verifikacija presudan korak u industriji gde je svaka greška izuzetno skupa i demistifikuje činjenicu da je Srbija, sa svojim centrima u Beogradu, Novom Sadu i Nišu, postala ozbiljan globalni "powerhouse" za razvoj najsavremenijeg hardvera. Fokus priče je na revolucionarnoj tehnologiji koju razvija Next Silicon, posebno na njihovom „Maverick 2“ čipu koji menja pravila igre u svetu superračunara i high-performance computinga (HPC). Saznaćete kako izgleda inženjerska avantura kreiranja hardvera koji se dinamički prilagođava softveru, rešavajući probleme energetske efikasnosti i brzine koje tradicionalni procesori (CPU i GPU) ne mogu da savladaju. Podržite nas na BuyMeACoffee: https://bit.ly/3uSBmoa Pročitajte transkript ove epizode: https://bit.ly/4cNdB9T Posetite naš sajt i prijavite se na našu mailing listu: http://bit.ly/2LUKSBG Prijavite se na naš YouTube kanal: http://bit.ly/2Rgnu7o Pratite Pojačalo na društvenim mrežama: FB: https://www.facebook.com/PojacaloRS/ IG: https://www.instagram.com/pojacalo.rs/ X: https://x.com/PojacaloRS LN: https://www.linkedin.com/company/pojacalo TikTok: https://www.tiktok.com/@pojacalo.rs

Diffusion models changed how we generate images and video—now they're coming for text.In this episode, we sit down with Stefano Ermon, Stanford computer science professor and founder of Inception Labs, to unpack how diffusion works for language, why it can generate in parallel (instead of token-by-token), and what that means for latency, cost, and real-time AI products.We talk through:The simplest mental model for diffusion: generate a full draft, then refine it by “fixing mistakes”Why today's autoregressive LLM inference is often memory-bound—and why diffusion can shift it toward a more GPU-friendly compute profileWhere Mercury wins today (IDEs, voice/real-time agents, customer support, EdTech—anywhere humans can't wait)What changes (and what doesn't) for long context and architecture choicesThe real-world way to evaluate models in production: offline evals + the gold-standard A/B testStefano also shares what's next on Mercury's roadmap—especially around stronger planning and reasoning for agentic use cases.Try Mercury + learn more: inceptionlabs.aiFor more practical, grounded conversations on AI systems that actually work, subscribe to The Neuron newsletter at https://theneuron.ai.

Moonshots host Peter Diamandis speaks with Ben Horowitz, cofounder and general partner at a16z, alongside regular cohosts Salim Ismail, Dave Blundin, and Dr. Alexander Wissner-Gross, about whether AI can or should be paused, what happened when Horowitz told a Biden administration official that regulating AI means regulating math, why crypto is the natural money for AI agents, and why the gap between AI capability and societal adoption may be wider than people think. This episode originally aired on Peter Diamandis's Moonshots podcast.   Follow Peter H. Diamandis on X: https://x.com/PeterDiamandis Follow Ben Horowitz on X: https://twitter.com/bhorowitz Follow Salim Ismail on X: https://twitter.com/salimismail Follow Dave Blundin on X: https://twitter.com/DavidBlundin Follow Dr. Alexander Wissner-Gross on X: https://twitter.com/alexwg Listen to Moonshots: https://www.youtube.com/@peterdiamandis   Stay Updated:Find a16z on YouTube: YouTubeFind a16z on XFind a16z on LinkedInListen to the a16z Show on SpotifyListen to the a16z Show on Apple PodcastsFollow our host: https://twitter.com/eriktorenberg Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

This Week In Startups is made possible by:Gusto - Try Gusto today and get 3 months free at http://uber.com/ai-solutionsCrusoe Cloud - Reserve your capacity for the latest GPU's at http://uber.com/ai-solutionsUber AI Solutions - Book a demo today at http://uber.com/ai-solutions*Today's show: It's a packed show! We've got YouTuber and Openclaw enthusiast Matthew Berman, Ryan Yaneli, founder of Nextvisit, and Jason Grad, founder of Massive! We're all in on Openclaw, but we have no doubts there's still room in the market for a GIANT Openclaw consumer app to shift the paradigm. What will that look like? Will it be an app? Will it be baked into the iPhone? Let's explore!**Timestamps:* 00:00 Intro02:04 Why Matthew thinks Openclaw is not ready yet to be brought to the consumer04:45 Jason doesn't want hundreds of different apps, and thousands of tabs05:45 Why Ryan sees open claw giving consumers access to opportunities they couldn't have gotten to otherwise.07:02 Only 10% of people are technical enough to install openclaw08:16 Would Openclaw be better off as an app?08:27 *Gusto*. Check out the online payroll and benefits experts with software built specifically for small business and startups. Try Gusto today and get three months FREE at [Uber.com/twist](http://uber.com/ai-solutions)00:10:52 The killer use case that could bring Openclaw to the consumer00:12:13 Why Meta acquired Manus.00:15:13 How Ryan uses Openclaw in his personal life00:18:44 *Crusoe Cloud*: Crusoe is the AI factory company. Reliable infrastructure and expert support. Visit crusoe.ai/savings to reserve your capacity for the latest GPUs today.00:23:24 What Jason's “Clawpod” does00:24:38 Jason demos his Openclaw workflow00:28:23 *Uber AI Solutions -* Your trusted partner to get AI to work in the real world. Book a demo with them TODAY at http://uber.com/ai-solutions00:30:04 How Matt used Openclaw to figure out he's been having stomach issues00:32:27 What will be the ultimate UX for AI?00:38:53 Anthropic has patched the ability to use Openclaw through its pro plan!00:42:20 Matt and Jason hope for a multi-model future — but we haven't made progress!00:52:21 Jason has skepticisms about the Openclaw foundation00:52:59 Ryan predicts a new Openclaw fork coming from the shadows!00:54:21 Peter Steinberger is going to OpenAI, NOT to work with Openclaw… Will he “orphan” openclaw?00:58:19 does raspberry AI stand a chance against Apple?*Subscribe to the TWiST500 newsletter: https://ticker.thisweekinstartups.com/Check out the TWIST500: https://www.twist500.comSubscribe to This Week in Startups on Apple: https://rb.gy/v19fcp*Follow Lon:X: https://x.com/lons*Follow Alex:X: https://x.com/alexLinkedIn: ⁠https://www.linkedin.com/in/alexwilhelm*Follow Jason:X: https://twitter.com/JasonLinkedIn: https://www.linkedin.com/in/jasoncalacanis*Thank you to our partners:*Gusto*. Check out the online payroll and benefits experts with software built specifically for small business and startups. Try Gusto today and get three months FREE at [Uber.com/twist](http://uber.com/ai-solutions)*Crusoe Cloud*: Crusoe is the AI factory company. Reliable infrastructure and expert support. Visit [crusoe.ai/savings] to reserve your capacity for the latest GPUs today.*Uber AI Solutions -* Your trusted partner to get AI to work in the real world. Book a demo with them TODAY at [Uber.com/twist](http://uber.com/ai-solutions)Check out all our partner offers: https://partners.launch.co/*Check out Jason's suite of newsletters: https://substack.com/@calacanis*Follow TWiST:Twitter: https://twitter.com/TWiStartupsYouTube: https://www.youtube.com/thisweekinInstagram: [https://www.instagram.com/thisweekinstartups](https://www.instagram.com/thisweekinstartups/)TikTok: https://www.tiktok.com/@thisweekinstartupsSubstack: [https://twistartups.substack.com](https://twistartups.substack.com/)