Stewart Squared

Stewart Alsop III reviews a broad range of topics with his father Stewart Alsop II, who started his career in the personal computer industry and is still actively involved in investing in startup technology companies. Stewart Alsop III is fascinated by what his father was doing as SAIII was growing up in the Golden Age of Silicon Valley. Topics include: - How the personal computing revolution led to the internet, which led to the mobile revolution - Now we are covering the future of the internet and computing - How AI ties the personal computer, the smartphone and the internet together

Stewart Squared is a business podcast hosted by Unknown Host, with 102 episodes on record and a Required Pod Score of 80. PitchCentric scores this show on Booking Probability, Listen Score, and live audience signals refreshed every 24 hours.

Unknown Host hosts Stewart Squared, a business show with 102 episodes published.

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  Episode #102

  Episode #102: AI Is Eating the World’s Memory

  Aug 13, 202652 min

  In this episode of Stewart Squared, Stewart Alsop III and Stewart Alsop II sit into the shifting ground beneath the AI boom, starting with the strange saga of Leopold Aschenbrenner's hedge fund and the memory chip shortage that's rippling through everything from Apple's product line to the gaming industry, before moving into how OpenAI and Sam Altman's data center spending is reshaping global compute demand, the widening gap between American and international tech ecosystems, China's uneasy relationship with open source AI, and a look at Mira Murati's new venture Thinking Machines Lab and its fine-tuning tool Tinker, wrapping up with some thoughts on what a live, interactive version of the show could look like. Timestamps 00:00 AI bubble and the Leopold Aschenbrunner hedge-fund story; debt , margin pressure, and why memory stocks surged 00:05 memory becomes the bottleneck as AI data centers expand; Apple, Micron, and rising RAM prices 00:10 Korea takes a hit from memory-market swings; contrast with Japan, manufacturing, and the karetsu model 00:15 Live translation tech, Google’s new API, and a side discussion of robotics and Japanese PC history 00:20 Japan’s early PC ecosystem, NTT, Microsoft, Windows, and why standards won the market 00:25 Back to finance: equity vs debt , leverage, Glass-Steagall, and how banking got reorganized 00:30 AI hedge funds, risk , Citadel, margin calls, and the distinction between lending and investing 00:35 Capitalism by starting companies vs buying companies; why money is partly a metaphor 00:40 AI agents , Chrome, and the difference between distributed and federated systems 00:45 Matrix and Nostr, open-source messaging, and the internet’s new borders 00:50 Live audience questions, interactive publishing, and the business potential of real-time conversation Key Insights The AI boom is straining a memory chip supply that can't scale fast enough. Massive spending on AI data centers by companies like OpenAI has driven demand for DRAM through the roof, and because memory factories take years to build, prices have roughly tripled in six months — squeezing everyone from Apple (which is struggling to ship products like the Mac mini) to the broader computer gaming industry. Leverage is what turned a smart bet into a crisis. Leopold Aschenbrenner's "Situational Awareness" hedge fund quadrupled investor money early on by going heavy into AI, but borrowing tens of billions against volatile chip and memory stocks left him exposed when prices tanked — prime brokers like Bank of America, Goldman Sachs, and JPMorgan Chase issued margin calls, and Citadel ultimately bought the distressed assets at a steep discount. Korea's economy is deeply entangled with the memory business. Companies like SK Hynix built Korea into a manufacturing powerhouse for chips, and that same concentration made its stock market especially vulnerable — the market reportedly fell more than 33% in July, a decline worse than the crashes of 1997 and 2015. Deregulation reshaped modern finance in ways still being felt today. The conversation traces a line from Glass-Steagall's separation of commercial and investment banking, through its effective rollback via the Gramm-Leach-Bliley Act, to today's financial holding companies (like JPMorgan owning both Chase and an investment bank) — blurring risk in ways that echo the AI/memory borrowing spiral. National tech ecosystems don't automatically follow American patterns. Japan's PC industry, dominated by NTT, never fully converged on the IBM-clone standard the U.S. market did, and Korea's cultural relationship to gaming and digital life (partly shaped by heavy state investment in nationwide internet infrastructure) diverged sharply from its neighbors, despite a shared heritage. China's relationship with open-source AI is shifting. Having initially embraced open source partly because it seemed easier to control than proprietary Western tech, China now appears increasingly concerned about having lost that control and is working to reassert it. Distributed, interoperable messaging protocols are having a moment. Tools like Matrix and Nostr (open-source alternatives gaining traction partly in response to Meta's restrictions) revive a decades-old dream of software systems messaging each other freely, distinguishing distributed "pub-sub" models from federated ones where all participants must cooperate.

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  Episode #101

  Episode #101: Apple's AI Is Finally Here. Why Does It Still Feel Broken?

  Aug 6, 202654 min

  In this episode of the Stewart Squared podcast, hosts Stewart Alsop and Stewart Alsop II dig into Apple's rocky iOS 27 rollout and the Apple Intelligence features that still don't quite work, before spiraling into Apple's org chart and headcount, the lost art of building apps, chip design and the Apple 100, open source versus closed source (MLX, the Linux kernel, GitHub vs. GitLab), Claude Code and why Anthropic's terminal-first approach might be winning the AI race, the commoditization debate around Chinese open source models, Adobe's fall from grace and the Postscript-to-Flash saga with Steve Jobs, real-time publishing and the Ben Thompson model for podcasting, and manufacturing hardware from PCBs to Sonos speakers. Timestamps 00:00 — iOS 27 rollout and buggy Apple Intelligence features frustrate both hosts. 05:00 — Debating Apple's headcount , retail vs. corporate split, and designers fleeing to OpenAI . 10:00 — The Apple 100 , the blurry line between research and development , and Xerox PARC . 15:00 — Apple's custom chips , open source roots like the Linux kernel and MLX . 20:00 — GitHub origins, Microsoft's enterprise mentality , and life since MS-DOS . 25:00 — Claude Code , Boris Cherny , and why terminal agents are reshaping coding. 30:00 — AI's text-based limits , Chinese open source models , and a coming robotics interview in Japan. 35:00 — GitLab vs. GitHub , what a workbench and compiling actually mean, and Mac performance gripes. 40:00 — Postscript vs. TypeScript , the Courier font , and early PageMaker newsletters. 45:00 — Hot type , the printing press , and why neither host went into industrial robotics . 50:00 — Editing Marine Business magazine and watching Japan and China take over manufacturing. 55:00 — Building PCBs , lessons from Sonos , and pitching a pay-to-listen real-time model. Key Insights Apple's biggest weakness isn't hardware or chips—it's software. Despite two years of hype, Apple Intelligence still creates duplicate calendar events and can't recognize things already scheduled, revealing a company that excels at silicon and operating systems but consistently ships mediocre apps, a gap the hosts trace back to Tim Cook's leadership. Apple's culture runs on a quiet meritocracy called the "Apple 100," a Steve Jobs-era concept where influence isn't tied to title—a junior engineer can be as pivotal as an executive, which explains how the company sustains innovation despite a bloated headcount of roughly 166,000, nearly half of it in retail. Anthropic's edge may not be model quality alone but its decision to build Claude Code around the terminal, treating programming as just another form of text prediction. This bet, credited largely to Boris Cherny, let Anthropic reach developers directly rather than waiting for polished consumer products. The commoditization narrative around AI cuts both ways. As Chinese open-source models close the gap with American closed-source ones, it either means nobody can maintain a lasting lead, or—as one host argues—the opposite: that leaders become nearly impossible to catch once compounding advantages set in. Adobe's arc from a lean systems company to what one host calls a fallen giant shows what happens when a company loses its performance-driven roots. Built on Postscript and page-description technology for the LaserWriter, Adobe eventually prioritized cross-platform reach over speed, echoing Apple's own struggles with app quality. Real-time publishing is emerging as a business model, not just a technical curiosity. Drawing on Ben Thompson's subscription-driven podcast network, the hosts float charging listeners for live access, turning the current ten-day publishing delay from a limitation into a monetizable feature. Manufacturing know-how doesn't transfer easily across domains. Lessons from Sonos scaling hardware in China, and earlier stories from Mercury Marine's engine factories, show that going from prototype to mass production—especially with physical components like PCBs and speakers—demands specialized expertise that even seasoned tech investors admit they lack.

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  Episode #100

  Episode #100: From Apple's iOS 27 to Anduril's Defense Tech: Where AI's Advantage Really Lies

  Jul 30, 20261h 4m

  In this episode of Stewart Squared, Stewart Alsop III sits down with his father and co-host Stewart Alsop II for a wide-ranging conversation that jumps from Apple's iOS 27 preview beta and the long road to Apple Intelligence, to the trust gap between Anthropic and OpenAI and the rise of digital-twin apps like Sentience, before pivoting into venture capital territory with a candid look at Andoril, Palmer Luckey, and the defense-tech boom reshaping how the primes do business; from there the two work through the surveillance creep of modern police tech, China's near-peer standing against the U.S. and its own reusable-rocket ambitions, and finally land on the state of fintech trust, the SpaceX IPO, and how thirty years of early-stage deal-making stack up against today's AI-driven venture landscape. Timestamps 00:05:00 — Apple Intelligence and the iOS 27 beta merge AI with hardware for an always-on assistant vision. 00:10:00 — Anthropic vs. OpenAI trust, Mira Murati's open-source push, and the Sentience digital-twin app. 00:15:00 — Apple's on-device security compared against Google and Microsoft. 00:20:00 — Tech billionaire philanthropy and legacy: Gates, Zuckerberg, and Jobs. 00:25:00 — Andoril and the personal story of investing alongside Palmer Luckey. 00:30:00 — History of defense-tech venture capital and Andoril's government ties. 00:35:00 — Palantir expanding into Argentina and the rise of predictive policing. 00:40:00 — Data, information, and wisdom in AI-driven knowledge management. 00:45:00 — Token minimizing strategy for running Claude and Codex coding agents. 00:50:00 — Fintech trust: Stripe, Venmo, PayPal, and the Panama Papers. 00:55:00 — SpaceX's IPO and public market valuation reflections. 01:00:00 — Reflexivity, Soros, and LLM token economics shaping VC decisions. Key Insights Apple's iOS 27 beta shows the company finally following through on the Apple Intelligence promise it botched two years ago, and its real advantage isn't the AI itself but that it's fused to hardware holding a user's calendar, messages, and contacts, letting it answer deeply personal questions Google can't match outside its own Pixel devices. Anthropic's positioning as "the Apple of AI" reflects a market increasingly sorting by trust rather than raw capability, with younger users drifting toward open-source alternatives like Mira Murati's newly funded startup, suggesting safety-focused branding alone won't hold loyalty across generations. Apps like Sentience, which build a "digital twin" by ingesting years of email, messages, and calendar history, hint at where personal AI is headed, but the gap between their mobile and desktop functionality shows this category is still early and unevenly built. Venture capital has quietly become the primary funder of military innovation, with firms like Founders Fund turning early bets on companies such as Andoril and Palantir into a broader industry rush toward defense and dual-use technology after decades of stagnant, cost-plus contracting among the traditional prime contractors. Predictive policing tools are reinforcing existing patterns rather than improving outcomes, since they're trained on historical data that sends more patrols into already over-policed neighborhoods, raising questions about transparency as governments adopt surveillance faster than citizens can question it. The venture capital game has shifted dramatically from early-stage bets to massive growth-equity checks, with average valuations jumping roughly ninetyfold over a decade as AI and defense deals now routinely reach into the billions, leaving classic early-stage investors feeling sidelined by their own industry. As AI agents multiply, the real competitive edge is shifting from model performance to token efficiency, with a "token minimizing" approach using multiple coding agents in parallel emerging as a practical way to build software at scale without hitting rate limits or runaway costs.

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  Episode #98

  Episode #98: What Apple Gets That the Rest of Tech Doesn't: Trust Scales

  Jul 16, 202657 min

  In this episode of the Stewart Squared podcast, host Stewart Alsop sits down with his father, guest Stewart Alsop II, to tackle a wide range of tech topics from AI chip design to cybersecurity vulnerabilities. The conversation covers OpenAI's Jalapeno chip (trained by AI in just nine months), the emerging etched.com platform, and Cloudflare's recent power move against Google, while Stewart shares his experience building real-time games on video calls and experimenting with ESP32 hardware for robotic projects. The discussion also dives into Meta's controversial KYC (know your customer) requirements that got Stewart kicked off Facebook and Instagram, Apple's evolution from hardware company to trusted computing partner under Tim Cook's leadership, the security implications of Chinese-manufactured ESP32 chips, and why hardware-focused companies struggle to adopt AI-driven development practices like using LLMs to eliminate software bugs—all wrapped up with an AI fact-check of their previous episode's claims. Timestamps 00:00 Stewart welcomes listeners and mentions his father's return from travels while he's been enjoying winter in Buenos Aires, setting up discussion topics including etched.com, OpenAI's Jalapeno chip training, and Cloudflare's competitive moves against Google 05:00 Discussion shifts to Meta's controversial KYC implementation and Supreme Court decisions allowing Facebook to require identity verification, with Stewart expressing strong opposition to Meta's practices and considering abandoning their platforms except WhatsApp 10:00 Conversation explores Mark Zuckerberg's personality and Meta's toxic culture, comparing their approach to Apple's user-protective stance and examining how tech companies handle personal data and privacy differently across their platforms 15:00 Deep dive into Apple's historical positioning as user-friendly company under Steve Jobs and Tim Cook, discussing their discipline in product management and how they've maintained consumer trust through consistent privacy protection over decades 20:00 Exploration of hardware complexity and software challenges, including Stewart's robotics workshop using ESP32 microcontrollers where even experienced engineers struggled with basic connectivity issues highlighting system complexity 25:00 Analysis of why hardware companies struggle adopting AI solutions, discussing Apple's bug management approach and questioning why they don't leverage tools like Anthropic's Mythos for eliminating persistent software bugs systematically 30:00 Security architecture discussion focusing on Apple's Unix-based kernel foundation inherited from NeXT, explaining how Avie Tevanian built security into macOS from the beginning making Apple relatively breach-free compared to competitors 35:00 Linux and Unix history explored, examining open source security models and discussing ESP32 operating systems, revealing that FreeRTOS provides embedded operating system functionality for these Chinese-manufactured development boards 40:00 Chinese semiconductor company Espressif discussion, examining potential vulnerabilities in using Chinese hardware while distinguishing between chip-level security and application-layer data access risks in connected devices 45:00 Device Authority company case study about remote device validation and firmware updates, connecting to historical cyberattacks like Stuxnet virus that physically infected Iranian centrifuges without internet connectivity 50:00 Cybersecurity industry overview mentioning Israeli company Check Point as pioneering firm, emphasizing importance of hiring specialized security experts rather than attempting DIY cybersecurity for critical business applications 55:00 Fact-checking segment reviewing previous episode claims about Dario Amodei's credentials, NVIDIA founding dates, software patents, OpenAI's Jalapeno chip timeline, and Waymo's highway incidents with minor corrections noted throughout discussion Key Insights 1. Apple has maintained user trust through a fundamental alignment with individual privacy rather than corporate interests. Unlike companies such as Meta and Microsoft, Apple has built its brand on protecting user data and maintaining security at the operating system level. This cultural commitment, formalized under Tim Cook but rooted in Steve Jobs' vision, has given Apple a distinct competitive advantage with over 2.5 billion users who feel the company is genuinely on their side rather than exploiting them for advertising revenue or data harvesting. 2. Meta is conducting controversial Know Your Customer verification processes that may represent a troubling expansion of identification requirements for social media platforms. Following what appears to be a 2025 Supreme Court decision, Facebook and Instagram are implementing KYC protocols previously reserved for financial institutions, potentially to legally collect identifying information for AI model training. This practice has driven some users to abandon Meta platforms entirely, viewing it as an unacceptable intrusion that violates the original spirit of personal computing. 3. Hardware-focused companies struggle to adopt AI coding tools because their engineering culture emphasizes control and deterministic systems. Companies like Apple, despite their technical sophistication, remain slow to implement AI solutions for tasks like bug elimination because their hardware-oriented workforce consists of control-oriented engineers uncomfortable with the probabilistic nature of AI systems. This cultural resistance prevents them from fully leveraging tools that could theoretically eliminate persistent software bugs that have plagued their ecosystem for years. 4. Security architecture fundamentally differs between operating systems, with Unix-based systems maintaining inherent advantages. Apple's security strength derives from the Unix kernel inherited from NeXT in 1997, which was designed with security as a core principle. This foundation underlies all Apple operating systems today, from macOS to iOS. In contrast, Microsoft's Windows has never achieved comparable security, making it constantly vulnerable to exploitation despite being the standard for government systems, which represents a significant ongoing risk. 5. The Chinese technology ecosystem, particularly in embedded systems and semiconductors, presents complex security considerations that are more political than technical. Companies like Espressif, which manufactures the ESP32 microcontroller chips, are headquartered in Shanghai and dominate the affordable IoT device market. However, because much of this technology uses open source software like Linux, the actual security risks are less about the hardware itself and more about higher-level software implementations that could potentially access personal data, making concerns somewhat overblown outside of China's Great Firewall. 6. The transition from traditional software development to AI-assisted coding is democratizing hardware prototyping in unprecedented ways. Non-engineers can now successfully program microcontrollers and build functional robotic systems using AI coding assistants, while ironically, experienced electrical and software engineers sometimes struggle with the same tasks due to their ingrained approaches. This represents a fundamental shift in who can participate in hardware development, though it also introduces new considerations around security and trustworthiness of the resulting systems. 7. Modern cybersecurity remains a constant race between attackers and defenders who possess equivalent knowledge and capabilities. The distinction between white hat and black hat hackers is merely one of intention rather than skill, as both groups operate with the same information simultaneously. Historical examples like the Stuxnet attack on Iranian centrifuges demonstrate tha...

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  Episode #97

  Episode #97: How AI Is Rewriting the Rules of Computing

  Jul 9, 20261h 3m

  Stewart Alsop sits down with his father, Stewart Alsop II, to unpack what chip design even means anymore, starting with OpenAI's Jalapeno chip and the wild claim that it was designed in nine months using their own LLM. They trace the CPU from its personal computer origins through GPUs, FPGAs, and the strange new world where anyone might vibe code a chip, then swing into digital projection and showrunner systems at TeamLab and Meow Wolf, autonomous vehicles and the LiDAR fight between Tesla and Waymo, Gaussian splats and world models, a detour into 3D printing and a failed IRL collectibles startup, and a closing stretch on patents, IP trolls, and whether China's open source AI push means the US proprietary model is losing ground. Timestamps 00:00 Apple chips, CPU vs GPU and why chip design feels virtualized now. 05:00 GPUs for video games, why productivity ignored graphics, and how the CPU became a bundle of multiple cores . 10:00 Team Lab and Meow Wolf as digital-projection worlds: projectors , microcontrollers, and the showrunner idea. 15:00 Interactive exhibits as “everything at once,” then a shift into Anthropic , biotech, and the pace of AI innovation . 20:00 Real-time systems, lipsync, world models , and why LLMs struggle with space-time . 25:00 Autonomous vehicles : Cruise , Waymo , LIDAR , Tesla’s camera-only approach, and the debate over edge cases . 30:00 More on Waymo vs Tesla , safety incidents, and whether Gaussian splats matter for robotics. 35:00 Chip design , OpenAI’s “jalapeño” chip , firmware , memory shortages, and why Apple memory costs are rising. 40:00 Patents , software IP , LLMs , and how China and the US diverge on open source versus proprietary AI. Key Insights The CPU has quietly become plural. What used to be a single processing unit is now many cores managing memory, disk, and networking all at once — the concept of "central processing" has essentially been virtualized from the inside out. Chip design may no longer require deep technical expertise. OpenAI's Jalapeno chip, reportedly designed in nine months using their own LLM, suggests that designing silicon is becoming something closer to "vibe coding" than a specialized engineering discipline. Digital projection systems like TeamLab and Meow Wolf run on lightweight computing, not heavy processing power. The magic comes from networked microcontrollers and a "showrunner" system, a concept borrowed from television, that keeps hundreds of projected events in sync without conflict. Tesla and Waymo represent two opposing bets on autonomy. Tesla relies purely on cameras and processing power, while Waymo loads its cars with LiDAR, radar, and video. Both approaches still hit real-world edge cases, from Waymo pulling cars off freeways after construction-zone incidents to a fatal Tesla crash with no clear explanation. World models are trying to give machines a sense of space and time. Gaussian splats, used by companies like Marble and Niantic, create detailed spatial reconstructions, but they're not yet real-time, which limits how directly they can be applied to something like robotic driving. Intellectual property often only reveals its value after failure. A collectibles startup pairing physical figurines with digital twins collapsed alongside the NFT market, but the conversation underscores how "IP trolls" and specialists like Nathan Myhrvold later mine failed patents for value nobody recognized the first time around. China's AI progress is closing the gap through an open source strategy the US mostly abandoned. Coupled with Anthropic's accusation that Alibaba scraped its codebase millions of times, the episode frames China's non-profit-driven, open approach as a real competitive threat to America's proprietary model.

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