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Stewart Squared

Stewart Alsop II, Stewart Alsop III

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

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  • 34 episodes
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  • Avg 55 min
  • English

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  • #115
    Thursday · 48 min

    Episode #115: Build God at $2 Trillion or Die Trying: Why Anthropic Should Ignore the Bankers

    In this episode of Stewart Squared, Stewart Alsop III and his father Stewart Alsop II tackle the wild intersection of AI valuations, robotics, and what they call "reality disturbance." Fresh off building a digital treasure hunt workshop that felt eerily close to the metaverse concepts they'd previously discussed, Stewart III shares his hands-on experience creating virtual worlds with Claude Code while the Pope's sound system tests echo in the background. The conversation spans Anthropic's controversial IPO pricing strategy (with Stewart arguing Dario Amodei should ignore the bankers and stick with a $2 trillion valuation), the surprising efficiency advantages of Claude over competitors like Codex, and how Ukrainian drone warfare is driving cutting-edge autonomous robotics development. They explore everything from the repeal of Glass-Steagall and its impact on tech IPOs to why consumer robotics powered by technologies like Niantic's Scaniverse are closer than most people think, all while debating whether Anthropic's technical superiority has created a cult-like following among developers who've abandoned other AI tools entirely. Key Insights 1. The podcast explores the concept of "reality disturbance" through a practical demonstration where the host created a digital treasure hunt in a virtual world that mirrors physical space. This exercise revealed how close we are to seamlessly blending digital and physical realities. The discussion expanded to include Anthropic's upcoming IPO, drawing parallels between how both virtual worlds and AI companies are fundamentally reshaping our understanding of what constitutes "reality" in business and technology. This convergence of digital simulation and artificial intelligence represents a significant shift in how we will interact with technology and each other. 2. Anthropic faces critical strategic decisions regarding its IPO valuation, with bankers reportedly advising valuations between 1.2 and 1.5 trillion dollars compared to the originally planned 2 trillion. The discussion suggests that founder Dario Amodei should ignore traditional banking advice and maintain the higher valuation, following Elon Musk's example with the SpaceX IPO. The argument centers on the idea that Anthropic's believers will buy the stock regardless of price because they understand the company's competitive advantages. Going public at 2 trillion would establish Anthropic as the generational AI company and could lift the entire tech sector by restoring market confidence. 3. The repeal of the Glass-Steagall Act in 1999 fundamentally changed the IPO landscape by allowing commercial and investment banks to merge, eliminating boutique investment banks that once took smaller companies public. This consolidation created upward pressure on IPO valuations, making it much harder for small companies to go public. Where companies in the 1980s and early 1990s could go public at valuations of a few hundred million dollars, today's threshold has risen dramatically. This shift has concentrated power in large banks and raised the scale a company needs to reach before going public, while the largest IPOs are now in the trillions. 4. Claude has achieved a significant but largely unrecognized competitive advantage in the AI market through superior efficiency and cost management. The host revealed that for just 100 dollars monthly, users get essentially unlimited access to Claude's capabilities for coding and development work, making it unnecessary to return to competitors like OpenAI's Codex. This pricing model, combined with Claude's technical superiority in real-time processing and token efficiency, represents a different competitive metric than traditional foundation model comparisons. Most market analysis has missed this shift, but enterprise customers and technical users who have discovered Claude's advantages have become quiet but devoted supporters who simply believe in the product without needing to advocate publicly. 5. Physical AI represents the next trillion-dollar opportunity according to Masayoshi Son of SoftBank, encompassing both industrial and consumer robotics applications. The discussion highlighted how technologies from companies like Niantic and Hover are creating the infrastructure for autonomous robots through detailed spatial mapping and digital twins. Industrial robotics is already deployed in warehouses and factories, but consumer robotics faces the challenge of operating autonomously in any environment without preprogramming. The Ukraine conflict has accelerated robotics development, with both sides deploying ground robots and drones that must operate in denied environments without internet connectivity, pushing the boundaries of true autonomy. 6. The accumulation of spatial data through technologies like Niantic's Scaniverse, built on Pokemon GO location data and enhanced with Gaussian splats, is creating a comprehensive digital map of the world that will enable robot navigation. This dataset, combined with similar indoor mapping capabilities, allows robots to understand and navigate both outdoor and indoor spaces. The convergence of AI language models with physical robotics and spatial computing represents a fundamental shift in how autonomous systems will operate. When public datasets of mapped spaces become available, robots will be able to navigate previously unknown environments, bringing consumer robotics significantly closer to practical deployment in homes and businesses. 7. Dependencies on AI platforms like Claude present both opportunities and risks for developers and businesses building on these systems. While Claude Code offers more openness than Claude Desktop or Web, it remains less open than truly open-source alternatives, creating potential lock-in effects similar to but more significant than historical browser dependencies. The portability issues with AI development environments differ fundamentally from earlier technology transitions because anything above the terminal level in these systems is not easily portable between platforms. This creates strategic considerations for developers who must balance the productivity gains from using cutting-edge AI tools against the long-term risks of platform dependency, with some planning eventual migration to locally-hosted GPU solutions when economically feasible. Timestamps 00:00 Workshop using digital world treasure hunt, Pope visit in background, discussing reality disturbance concept and its current relevance 01:23 Bankers advising Anthropic on IPO valuation reduction from 2 trillion, comparing to Musk's approach of ignoring banker advice and maintaining high valuations 05:14 History of IPO pricing as forecasting rather than free market, discussing uncle's Progress Software experience and banker control before public trading begins 06:39 Comparing Progress Software to Anthropic valuations, explaining rule of 40 metric combining net profit per share and growth rate for IPO readiness 10:35 Glass-Steagall Act repeal in 1999 enabling commercial and investment bank mergers, creating upward pressure on IPO valuations and company sizes 14:26 Why Anthropic should go public at $2 trillion, Dario Amodei and "build god or die trying," and why a $2 trillion debut would make Anthropic the generational AI company 20:12 Ukraine war robots operating in denied environments without internet, Masayoshi Son predicting physical AI as next trillion dollar opportunity in both factory and consumer markets 28:12 Niantic's Scaniverse technology mapping spaces using Pokemon GO data and Gaussian splats, enabling robot navigation through publicly posted digital twins 31:47 Claude's competitive advantage in real-time processing and token costs, analyzing Anthropic's IPO timing and insider shareholding patterns after six month lockup 39:29 Personal dependency on Claude Code for building projects, discussing switching costs between AI coding assistants compared to browser switching ease in the nineties 43:44 Apple integrating Xcode into Claude Code for iOS app development with simulator access, connecting three d world simulation to robotics training and Siri's limitations with Apple's weak native apps Links Eric Levin

  • #114
    November 5 · 55 min

    Episode #114: Moonwalks Cost Extra: Adventures with a $3000 Chinese Robot Dog

    In this episode, host Stewart Alsop welcomes back his father, Stewart Alsop II for another wide-ranging conversation that touches on everything from robotic dog demonstrations in Buenos Aires to the intricacies of Anthropic's IPO filing. The two dive into Anthropic CEO Dario Amodei's disciplined strategic approach, dissecting the company's unusual corporate governance structure featuring five stock classes and a public benefit oversight board. They explore how Anthropic has maintained profitability while renting data center capacity rather than building their own infrastructure, contrasting this with OpenAI's more scattered approach. The discussion weaves through comparisons of iconic tech founders, the nature of strategic partnerships versus customer relationships, and culminates in examining Elon Musk's risky bets across Tesla and SpaceX, including his pivot toward humanoid robots and satellite-based data centers. Key Insights Here are 7 key insights from this episode: 1. Anthropic demonstrates exceptional strategic discipline compared to its competitors, particularly OpenAI. The company has focused on enterprise customers from the start rather than consumer chatbots, evident in their customer base where they have numerous clients spending over a million dollars annually and many more at six-figure levels. Unlike OpenAI which built its own data centers, Anthropic leases capacity from others, including a notable arrangement with SpaceX AI for 13 billion dollars per quarter. This disciplined approach has allowed them to reach near break-even on an operating basis by the second quarter, proving their business model works without burning excessive capital. 2. The episode establishes that Dario Amodei possesses the rare quality of strategic vision that marks generational CEOs. Strategy differs from tactics in that it concerns what happens five or more years out, not just next quarter. The comparison is made to figures like Steve Jobs who could see the future, contrasted with Bill Gates who was brilliant tactically but failed to anticipate major shifts like the Internet and AI. Dario left OpenAI with a clear vision, built a team around it, and has remained remarkably consistent in executing that strategy over five years, suggesting he has this critical long-term thinking ability. 3. Anthropic has created an unprecedented governance structure featuring five classes of stock and a two-layer control system that has never been seen before in tech IPOs. There is a five-member long-term benefit trust structured as an LLC whose board members receive no equity, just 100,000 dollars annually, tasked with ensuring the company fulfills its public benefit mission around AI safety. Meanwhile, Dario and four other founders control 50.1 percent through special stock that trumps everything else. This complex structure reflects the unique challenge of governing a company trying to build superintelligence while ensuring it remains safe. 4. The robotics industry is experiencing a fundamental shift where software has no moat, which has major implications for both hardware makers and foundation model labs. Chinese company Unitree is leading in accessible robotics with products ranging from 3,000 dollar dog robots to 17,000 dollar advanced models and humanoid robots, all with open software. The discussion reveals that reactions to robots vary culturally, with Americans more fearful due to media like Black Mirror, while Argentines show greater curiosity. This suggests the robot adoption curve may vary significantly by geography and cultural factors. 5. The competitive landscape in AI is consolidating around a few key players with different approaches. Anthropic explicitly names only OpenAI and Google as competitors in their IPO documents, not naming Meta despite their significant AI work. The partnership structures matter enormously, with Anthropic partnering with Amazon, Google, Microsoft and Nvidia while avoiding building their own infrastructure. Meanwhile, SpaceX AI built massive data center capacity for Grok but found they did not need it all, allowing them to rent it to Anthropic and transform their revenue from 24 billion annually to potentially 40 billion plus with just this one arrangement. 6. The episode reveals that established tech companies are struggling with strategic transitions as the personal computing era ends. Microsoft failed to anticipate both Linux and mobile phones despite their Wintel partnership dominance. Current PC sales are described as apocalyptic except for Apple products. The Wintel partnership itself, while successful, was marked by conflict because both Microsoft and Intel wanted monopoly control in their domains, showing that even successful partnerships involve significant tension and compromise when companies must find common ground between different strategies. 7. There is an emerging theme that the most successful bets in tech involve believing in specific visionary founders rather than safe, diversified strategies. The example is given of someone investing 80 percent in Tesla and 20 percent in Apple using a buy, borrow, die strategy, which works specifically because of belief in Elon Musk's ability to execute risky bets like pivoting Tesla entirely to humanoid robots. Similarly, Google is beginning to put AI compute in space with TPUs on satellites, while Schmidt separately builds rockets through Relativity Space, showing how the new competitive landscape requires finding and backing founders with legitimate long-term vision in uncertain domains. Timestamps 00:00 Robot dog demo, and why Argentines are curious while Americans are afraid 03:27 Waymos in San Francisco and Unitree's $3,000 dog vs the $17,000 one 06:12 Chef Robotics: robots that pack food, and why robot software has no moat 10:07 The Anthropic IPO: building god or dying trying, and renting compute from SpaceX AI 14:19 Enterprise customers vs chatbot users, and Opus 5.5's new pricing 16:33 OpenAI went out to raise $30 billion and got $11 billion 18:02 The S-1, the five classes of stock and a board with no equity 20:39 Character analysis: who is Dario Amodei? 21:47 Strategy vs tactics: Atlassian, Jobs, Gates and Zuckerberg 27:32 Back to Dario: discipline, leased data centers, near breakeven 29:25 Governance: the long-term benefit trust and the 50.1% founder stock 35:15 Only two competitors named in the S-1: OpenAI and Google 36:23 Partners vs customers: who pays Anthropic 42:28 Wintel: a partnership full of conflict 44:27 PC sales collapse and Apple's touchscreen MacBook 45:44 80% Tesla, 20% Apple: buy, borrow, die 47:48 Elon's bet on humanoid robots and data centers in space 50:12 Google's TPUs in orbit and Rocket Lab

  • #113
    October 29 · 57 min

    Episode #113: The Infinite Slop Cannon in the Sky: How Elon's Building His Empire

    In this episode of the Stewart Squared podcast, host Stewart Alsop and his son Stewart Alsop II explore the convergence of autonomous robotics, corporate consolidation, and network infrastructure. The conversation kicks off with speculation about a potential Tesla-SpaceX merger, drawing parallels to Japanese keiretsus and examining how Elon Musk might be building what they dub "Musk X"—a unified empire spanning rockets, AI, autonomous vehicles, and satellite networks. From there, they venture into the world of dog bots and robotic operating systems, with Stewart II demonstrating real-time AI-generated web content and discussing his plans to build mesh networks for dance-battling robots. The elder Alsop brings decades of venture capital perspective to bear on topics ranging from Amazon's distribution superiority over Walmart to Salesforce's bold pivot toward agent-based pricing, while they both grapple with fundamental questions about what constitutes an operating system in an age where the boundaries between physical robots, digital networks, and creative tools are rapidly dissolving. Key Insights Here are seven key insights from this episode: 1. Elon Musk appears to be consolidating his various companies into what could be called a modern American keiretsu, similar to the Japanese and Korean conglomerates that organized multiple industries under unified control. The recent Tesla Roadster demo featuring SpaceX thrusters signals a potential merger between Tesla and SpaceX. This consolidation strategy would create an integrated empire spanning rockets, satellites, AI, solar energy, tunneling, and automotive technologies. Musk seems to be moving toward a single company structure that allows investors to bet on him personally rather than individual business units, though the complexity makes it difficult for analysts to properly value the combined entity. 2. The concept of operating systems is evolving beyond traditional definitions as technology moves into robotics and autonomous systems. Historically, operating systems were tied to specific hardware architectures with CPUs and memory, but now the term is being applied loosely to networks and platforms. For robots and autonomous vehicles, operating systems must manage both local hardware and network connectivity simultaneously. Companies like Amazon have developed fleet level coordination systems that function as operating systems for managing thousands of warehouse robots, while Tesla and Waymo have built proprietary systems for their vehicles. The emergence of robotic operating systems represents a fundamental shift in how we think about computing infrastructure. 3. Amazon has successfully built a purpose designed distribution system that competitors like Walmart struggle to match because Amazon anticipated future needs rather than modifying existing infrastructure. Amazon created an ontology that modeled people, warehouses, and distribution centers as an integrated system from the beginning. Walmart faces difficulties because their distribution system was originally designed to serve stores rather than individual consumers. This demonstrates how companies that build systems with future scalability in mind gain significant competitive advantages. Amazon's ability to deliver products next day even in smaller cities like Santa Fe illustrates the power of this forward thinking approach. 4. The economic models for AI companies remain unclear and unstable, with Character AI becoming potentially the first major AI casualty by filing for bankruptcy despite user engagement. Many companies that built applications on top of foundation models are now struggling as those foundation models absorb market value. Salesforce represents an interesting case study as CEO Marc Benioff pivots the entire business model from traditional SaaS subscriptions to charging for AI agent usage and token consumption. This willingness to abandon the old business model before being forced to shows sophisticated awareness of the innovator's dilemma. However, the rapid deflation in API pricing across the industry makes long term revenue modeling extremely challenging. 5. Intellectual property protections are fundamentally changing in both technology and creative fields due to AI capabilities. In technology, proprietary data is replacing patents as the primary defense mechanism since foundation models cannot be easily patented. In creative industries, the core issue is whether AI generated content can be copyrighted when the fundamental concept of copyright assumes human creation. Companies like Adobe, Canva, and Figma that built businesses around design tools now face existential threats because foundation models can generate similar outputs directly. The traditional boundaries between fair use, training data, and copyright infringement remain legally and philosophically unresolved. 6. Mesh networks are transitioning from theoretical concept to practical implementation after decades of being discussed but not realized. A mesh network allows all devices to communicate at equal levels with routing capability distributed across the entire system rather than centralized. Starlink represents a functioning mesh network with satellites constantly moving through space while coordinating coverage and load distribution. The technology enables new applications like coordinated robot systems and autonomous vehicle fleets. However, successful implementation typically requires a single visionary leader with technical depth and resources to force the coordination to happen, which partly explains why competitors struggle to replicate systems like Starlink. 7. The creative capabilities enabled by AI tools are producing unprecedented outputs at minimal cost compared to just two years ago. Work that would have required hiring specialized developers and cost tens of thousands of dollars can now be accomplished overnight by individuals. This democratization of creative and technical capabilities is creating new business opportunities while simultaneously making existing business models obsolete. The challenge lies in finding sustainable economic models when production costs approach zero. Companies must navigate between physical and digital experiences, as demonstrated by immersive experience companies like Meow Wolf potentially facing their own innovator's dilemma as digital technologies advance. Timestamps 00:00 Welcome and episode overview discussing dog bots, Chinese spyware containment, and potential Tesla SpaceX merger speculation following recent demo 05:00 SpaceX going public raises questions about Elon building Japanese keiretsu-style empire consolidating all his companies under single Musk Inc entity 10:00 Exploring keiretsu concept from Japan's postwar economy with horizontal financial clusters and vertical supply chain models like Mitsubishi and Toyota 15:00 Flying Roadster demo was pure stunt using SpaceX thrusters, signaling possible merger while Elon battles SEC over public company regulations 20:00 Robotic operating systems explained as network and hardware coordination requiring autonomy, with Amazon building massive warehouse fleet management systems 25:00 Amazon's purpose-built distribution ontology beats Walmart's store-focused model, demonstrating importance of modeling people not just physical locations 30:00 Linux forks power everything from Android to Starlink satellites as operating systems evolve for multicore CPUs and autonomous robot coordination 35:00 Character AI bankruptcy shows no economic model despite user obsession, while IP protection shifts from patents to proprietary data 40:00 Creative abundance enables rapid website development and potential automation consulting for Argentine custom automotive manufacturers using robotic implementations 45:00 Salesforce pivots to agent-based token pricing abandoning traditional SaaS, while Adobe faces existential threat from foundation models replacing design tools 50:00 Mesh networks evolving from thirty-year concept to reality through Starlink's satellite coordination, with Tesla collecting data versus Waymo's command network 55:00 Next episode promises DogBert demo showcasing mesh network between robot dog, ESP32 microcontrollers, and Raspberry Pi establishing distributed device communication

  • #112
    October 22 · 51 min

    Episode #112: From the PC to the Robot Dog: How Do Normal People Get Through the Next Shift?

    In this episode of the Stewart Squared podcast, Stewart Alsop III sits down with his father, Stewart Alsop II, to unpack the intersection of AI agents and robotics, kicking off with Stewart III's upcoming hands-on experience with a four-legged dog robot running open-source software. The conversation ranges from the fragile state of current AI autonomy—distinguishing between truly autonomous systems and what are essentially sophisticated loops—to the practical challenges of implementing agents like Muse, Hermes, and Claude in daily life. They explore why industrial robots remain automated rather than autonomous, the security implications of Chinese hardware in American robotics (including Waymo's approach), and AMD's massive $8.2 billion acquisition of World Labs to compete with Nvidia's dominance. Stewart Alsop II brings his decades of tech industry perspective to bear on how normies will adapt to this transition, drawing parallels to previous technological shifts while acknowledging this one's unprecedented speed and scope, while Stewart III shares his experiences with vibe coding and the reality that sim-to-real robotics remains largely promotional material aimed at VCs rather than functional autonomy. Key Insights 1. The challenge of autonomy in artificial intelligence and robotics lies in the fact that what companies promise as autonomous systems are actually just sophisticated loops in code rather than true autonomy. In robotics, there are two key concepts: sim to real and real to sim, with sim to real consistently failing because you cannot successfully transfer simulations directly to real-world robot operations. Current agents from companies like Muse, Hermes, and OpenAI use brute force loops to create the appearance of autonomy, but they lack genuine autonomous decision-making capability. 2. The trust and security concerns surrounding AI agents represent a fundamental problem for widespread adoption, particularly when these agents require access to sensitive personal information. Services like Instinct ask users to authorize full access to email accounts including deletion privileges, while platforms like Muse operate without clear boundaries between user authorization and autonomous action. The recommendation from AI systems themselves, such as Gemini, suggests using intermediaries or read-only access rather than direct account connections, highlighting the inherent security risks in current agent implementations. 3. Meta has taken a strategic lead in consumer-facing AI agents by launching Muse as a mobile application rather than just a web-based service or foundation model feature. This represents a natural evolution for Meta given their success with mobile apps like Facebook and Instagram. By creating a dedicated app with personality features and visual representations, Meta has positioned itself ahead of competitors like OpenAI's dots, though this comes with significant risks as the average consumer may not understand the security implications of granting such broad access to an AI agent. 4. The distinction between automation and autonomy is critical for understanding the current state of robotics. Industrial robots that have been successfully deployed in factories for years are automated, meaning they are hard-coded to perform specific tasks reliably and quickly, but they require reprogramming for any changes. True autonomy, where robots can adapt and make decisions independently in changing environments, remains largely unrealized. The demos circulating on social media are typically either teleoperated or functioning in highly controlled conditions, designed primarily to attract venture capital rather than demonstrate practical autonomous capability. 5. Physical AI represents the next major investment wave as capital seeks new outlets beyond traditional large language models. Companies like AMD's acquisition of World Labs for 8.2 billion dollars and the emergence of spatial computing companies like Niantic demonstrate this shift. The hype around physical AI is drawing capital that previously flowed into data centers, but there is uncertainty about whether these investments will deliver on their promises or simply continue a pattern of high-capital-velocity speculation without delivering practical autonomous systems. 6. The transition to an AI-integrated world will be challenging but manageable for average people, similar to how society adapted to previous technological shifts like personal computers, the internet, and mobile devices. However, this transition is happening much faster than historical changes, compressing what might have taken 180 years of industrial evolution into approximately 10 years. Digital natives who are growing up with AI will have advantages, but even industry leaders like Sam Altman and Dario Amodei are making educated guesses rather than operating from certain knowledge about how this future will unfold. 7. Government and bureaucratic institutions will be the last to successfully integrate AI technologies, even as they face increasing pressure to modernize. The structural collapse of bureaucratic efficiency is not apocalyptic but represents a difficult transition period where institutions struggle with outdated paper-based systems and liability concerns that generate excessive red tape. Local governments face particular challenges due to limited resources, as illustrated by county-level water policy committees trying to manage complex issues with minimal staff and budgets, suggesting that full integration of AI into government operations may take a decade or more. Timestamps 00:00 Introduction of podcast covering Anthropic IPO timing for November and discussion of Muse agents versus Hermes open source framework as agent harnesses 07:46 Conversation shifts to autonomy challenges in robotics, explaining sim-to-real simulation failures and how AI companies promise autonomy through code loops rather than true independence 12:46 Discussion of trust issues with AI agents accessing personal data, comparing Gemini's advice about financial account security and the risks of granting write access to applications 17:46 Debate over transparency problems with autonomous systems and black box technology, noting how foundation models create understanding gaps even for developers building with them 22:22 Analysis of Chinese hardware dominance in robotics manufacturing, Waymo's strategy replacing Chinese firmware with American software, and Connected Vehicle Rule banning Chinese components 27:22 Examination of bureaucratic collapse across institutions, comparing American and Argentine regulatory burdens while discussing local government water policy challenges in Santa Fe County 32:22 Exploration of automation versus autonomy distinction in factory robots, AMD's acquisition of World Labs for physical AI development, and Niantic's spatial intelligence advantages 37:22 Discussion of physical AI as next investment frontier, robotics hand manipulation challenges, and reality disturbance concept where digital creativity merges with physical world 42:22 Reflection on normies adapting to AI transitions, comparing current changes to historical industrial revolution, and examining how ChatGPT companions represent bigger shift than coding tools 47:22 Final thoughts on generational transitions affected by AI, podcast mission exploring age perspectives, and conclusion that autonomous agentic robots remain distant future goal

  • #111
    October 15 · 51 min

    Episode #111: Anthropic's IPO Comes With an Existential Risk Warning

    In this episode of the Stewart Squared podcast, host Stewart Alsop sits down with his father, Stewart Alsop II, to dig into Anthropic's IPO filing, whose S-1 prospectus reached Reuters before the public could read it. Stewart Alsop II explains how confidential SEC filings work and why the company can't talk about the offering, and the two unpack the risk factors Reuters quoted: roughly 80 of 261 pages, including warnings of catastrophic or existential risk from self-preservation behavior, evaluation awareness and late-emerging capabilities, which Stewart believes is a first for any IPO. Stewart Alsop II draws a parallel between Anthropic's constitution for Claude and the founding of American democracy: both design a system instead of trying to control the outcome. The conversation moves through how Morgan Stanley and Goldman Sachs sell an offering to family offices and endowments, primary versus secondary shares, and why the company most focused on safety has produced what Stewart considers the best and cheapest model on the market, Claude Opus 5.5, which he used to build a live website during the recording. Both remain skeptical of the apocalyptic scenarios: Stewart Alsop II can't see code in a data center taking control from people, and Stewart points to AI psychosis and cybersecurity as the real near-term risks. Key Insights 1. Anthropic's IPO filing, as reported by Reuters, devotes roughly 80 of its 261 pages to risk factors, nearly twice the 48 pages describing the business. Among them are warnings that advanced AI could pose catastrophic or existential risk to humanity, citing self-preservation behavior, evaluation awareness (models behaving differently when they know they are being tested) and late-emerging capabilities that appear during training. Stewart believes this is the first time a company going public has cited existential risk; Stewart Alsop II allows that a biotech company might once have come close. 2. Stewart Alsop II frames the difference between Anthropic and its competitors as designing a system versus controlling an outcome. Meta, OpenAI, Grok and others try to control directly what their models produce; Anthropic wrote a constitution for Claude and hired a philosopher to shape its ethical structure. He compares this to the United States in 1776, which created a system of checks and balances rather than dictating outcomes. Stewart adds that the reinforcement learning from human feedback he once helped produce is itself a way of controlling model output. 3. Confidential filing is standard practice: a company works out its offering and its disclosures with the SEC privately, and must make the S-1 public about fifteen days before the roadshow under the JOBS Act. Once it has filed, the company cannot discuss the offering or make material moves, which is why nobody at Anthropic can explain how Reuters got the draft. SpaceX followed the same path and disclosed its option to buy Cursor before going public. 4. An offering of this size, reportedly about $100 billion at a $2 trillion valuation, needs the whole banking industry. Stewart Alsop II estimates a lead underwriter such as Morgan Stanley or Goldman Sachs puts 40 to 50 people directly on the deal, with hundreds more briefed to sell it. Once the S-1 is public, bankers call chief investment officers at family offices, endowments and institutions, share the prospectus and negotiate allocations. The bankers don't have to pitch the existential risk themselves: the S-1 carries that load. 5. Anthropic's Claude Opus 5.5, released a week after OpenAI's expensive Astra, is in Stewart's experience both the best and the cheapest model available; he used it to build a live website during the recording. That the company most focused on safety has produced the most capable model has made him question his skepticism about recursive self-improvement, though not about existential risk. 6. Both hosts see the real near-term risks as psychological and cybersecurity threats rather than apocalyptic scenarios. Stewart points to OpenAI's GPT-4o, whose personality led some users, including an OpenAI investor, into what he calls AI psychosis. Stewart Alsop II refuses agent apps that ask for full control of his email and calendar, and can't see a scenario in which code running in a data center takes control from people. Stewart predicts that half of what the S-1 warns about is science fiction; Stewart Alsop II replies that the company has to disclose it in case it happens. 7. The question of controlling outcomes versus designing systems reaches beyond AI. Stewart Alsop II worries that American politics has shifted from caring about the system to trying to control outcomes, such as calls to tax billionaires. He distinguishes opinion from reporting, praising the New York Times for its deeply reported story on Anthropic's meetings with religious leaders, made possible when Anthropic released participants from their NDAs. Timestamps 00:00 Anthropic's IPO and the S-1 that reached Reuters first 01:01 Confidential filings, the SEC and the SpaceX–Cursor precedent 02:50 The New York Times, Chris Olah and the Vatican 05:02 80 pages of risk factors, including existential risk 07:02 Alignment and the Skynet scenario 09:34 Why Anthropic split from OpenAI 13:58 "Is Claude alive?" Stewart Alsop II reads Claude's answer 16:59 Losing control, and RLHF as censorship 18:29 Designing the system vs. controlling the outcome: a constitution like 1776 20:04 Astra vs. Opus 5.5, and building a website live 21:54 Why Stewart Alsop II doesn't think AI will take control 24:19 Who leads a $2 trillion IPO: Morgan Stanley, Goldman Sachs, JPMorgan 28:17 Inside the banks: roadshows, family offices and chief investment officers 33:39 Funding rounds, primary vs. secondary shares and board approval 38:44 Controlling outcomes in politics, and reporting vs. opinion 42:54 OpenAI, Gemini, Meta's Muse and Microsoft's relevance 45:51 GPT-4o, AI psychosis and cybersecurity: the real risks 47:18 The website the LLM built during the recording 48:32 A gifted robot, Chinese firmware and Dimensional OS 50:22 Join us live: the WhatsApp group Links Join the Stewart Squared WhatsApp group

  • #110
    October 8 · 51 min

    Episode #110: Trust, Debt, and Foundation Models: The New Global Competition

    In this episode of the Stewart Squared podcast, host Stewart Alsop sits down with his father Stewart Alsop II to explore the rapidly evolving landscape of AI and foundation models, with a particular focus on Anthropic's anticipated IPO. The conversation spans from Stewart's latest adventures in self-hosting and creating sizzle reels with AI to the seismic shift brought by Anthropic's release of Claude Opus 5.5—a model that's both cheaper and more capable, fundamentally changing the economics of AI services. The discussion takes a historical turn as they compare today's AI competition to past tech IPO races, from the 1986 cluster of Oracle, Microsoft, and Sun going public within days of each other to the more recent Uber-Lyft showdown. Stewart and Stewart dig into how Anthropic's potential first-mover advantage in going public could allow them to define the competitive landscape before OpenAI, drawing parallels to how Apple and Microsoft established themselves in different eras. The episode also tackles bigger questions about state power, reserve currencies, and how foundation model companies like Anthropic and OpenAI are positioning themselves as modern equivalents to the international corporate powerhouses of previous generations, all while the technology continues its relentless march toward making programming accessible to everyone through what Stewart calls "the new BASIC." Key Insights 1. The foundation model companies are experiencing rapid commoditization as competitive pressures force them to release better models at lower prices. Anthropic released Opus 5.5, which is both high quality and cheap, effectively making expensive subscriptions unnecessary. This represents an implicit admission that companies can no longer maintain the business model of very expensive models, particularly after OpenAI's competitive releases. The training costs are becoming a smaller component of the overall service compared to inference costs, which changes the economics significantly. Companies are now competing on price while maintaining similar capabilities, suggesting the fundamental functionality of these models is becoming commoditized. 2. Anthropic has a strategic advantage in going public first because they can define the elements of competition for foundation model companies. By dropping their S-1 filing before OpenAI, they will establish how investors should evaluate these companies, present financial metrics, and position the market before their competitor. This is similar to historical IPO races, such as the 1996 search engine rush when Lycos, Excite, and Yahoo went public within days of each other. However, unlike those examples where none became the ultimate winner, this race matters because Anthropic can establish themselves as a public company while OpenAI remains private. 3. Anthropic has maintained remarkably consistent messaging around safety and responsible AI development, which builds trust with customers and investors. Since the founding team left OpenAI specifically over safety concerns, they have stayed focused on that message despite some pricing missteps. This consistency mirrors Apple's approach more than Microsoft's scattered strategy. OpenAI, by contrast, has pursued hardware projects, advertising, and various other initiatives that create uncertainty about their core mission. This fundamental positioning difference could prove crucial for their respective IPOs and long term success. 4. The scale of technology companies has expanded dramatically since the PC era IPOs of the 1980s. Apple went public at a market value of 1.8 billion dollars and Microsoft at 780 million dollars, while today's companies like OpenAI are valued around 300 billion dollars. This transformation occurred primarily because of the Internet, which connected billions of people globally rather than serving niche markets of millions. The unit volumes expanded from millions in the PC industry to hundreds of millions on the early Internet to billions today, with companies like Facebook and Apple each serving approximately 2.5 billion people. 5. Microsoft became a form of state power in the 1990s when Bill Gates was treated as a visiting head of state by foreign governments, including being requested to meet with the premier of China. This represented a shift where technology companies gained influence comparable to nation states. Today, companies like Meta are exercising similar power, with Zuckerberg directly interfering in Brazilian elections by allowing AI political ads against local law. The relationship between corporations and governments has evolved into a complex mixture where companies like OpenAI and Anthropic are entering this space of international corporate power. 6. Vibe coding represents the current generation's entry point for non technical people to create software, similar to how BASIC enabled beginners to program personal computers and HTML allowed people to create websites. This phenomenon of progressively higher level interfaces between computers and people occurs in every technology generation. Today, prompt engineering has become the new BASIC, allowing people to build functional software through conversation with AI models. This accessibility is expanding who can create technology, though the vast majority of the 8 billion people on Earth still just want to use technology rather than build it. 7. The US dollar's status as the reserve currency creates unique dynamics for American companies going public. The US government can print money to service its debt since it borrows in its own currency, unlike other nations that must borrow in dollars. However, this creates inflation risk where the government could devalue the currency to manage debt. Historical examples from Britain and the Dutch Republic show how reserve currency status can be lost through mismanagement. This macroeconomic context affects how investors should evaluate long term investments in American technology companies like Anthropic. Timestamps 00:00 Welcome and discussing Stewart's new ability to create audio visual content, specifically a sizzle reel constructed from Google Photos for robotics courses. 05:00 Claude Anthropic released Opus 5.5 which is remarkably cheap and high quality, reducing reliance on expensive models and changing competitive dynamics with OpenAI. 10:00 Discussion of how training costs are becoming smaller component of foundation model economics while inference margins improve, affecting IPO valuations. 15:00 Anthropic racing to IPO first will let them define competition terms before OpenAI goes public, examining historical tech IPO races like Lyft-Uber and 1996 search engines. 20:00 Comparing Apple and Microsoft's 1980s IPOs at much smaller valuations versus today's trillion-dollar foundation model companies and how Internet transformed scale. 25:00 Exploring how US dollar as reserve currency affects tech valuations and examining historical cases like Britain losing sterling dominance after World Wars. 30:00 Microsoft becoming state power player with Bill Gates meeting world leaders, and how Anthropic enters this realm of international corporate influence. 35:00 Zuckerberg interfering in Brazilian elections shows corporation-government power dynamics, Trump administration's selective corporate relationships with companies like Tether. 40:00 Anthropic maintains consistent safety messaging versus OpenAI's scattered approach, comparing to Apple's focus versus Microsoft's everything strategy. 45:00 Vibe coding as modern equivalent of BASIC programming, making AI accessible while service-as-software emerges as non-scalable customized consulting model disrupting traditional tech.

  • #109
    October 1 · 1 hr 4 min

    Episode #109: Are We Building the Dark Fiber of AI?

    In this episode of the Stewart Squared podcast, host Stewart Alsop sits down with his son, Stewart Alsop II, to unpack Anthropic's anticipated IPO and the seismic shifts happening in AI right now. The two dive into whether Anthropic will actually go public despite the existential risk narrative Dario Amodei has been pushing, exploring the real reasons these companies need to IPO (hint: it's about the cash burn). They discuss California's new AI regulations under Gavin Newsom, the emerging threat of JEV—a new AI architecture from a company called Hyde that could upend the entire foundation model business—and why Anthropic might be positioning itself around biotech IP rather than AI patents. The conversation weaves through regulatory capture, the impossibility of patenting a black box, NVIDIA's vulnerabilities, and why comparing AI to cars or airplanes misses the mark entirely. For more on the Verge interview with former FTC Commissioner Jonathan Cantor that Stewart Alsop references, check out The Verge's podcast feed, and if you want to explore JEV further, search for their "build prod, not god" positioning at their company site. Key Insights 1. The Anthropic IPO is expected to proceed despite concerns about existential AI risk because the company and its banks have assessed liability issues and determined they can move forward with the offering. Both Anthropic and OpenAI need to go public to access capital, as they are consuming cash at rates that will eventually push private investors to demand public markets access. The companies can raise additional private rounds, but going public provides access to much larger pools of capital through both initial offerings and secondary offerings that would be necessary to sustain their operations over time. 2. Product liability is emerging as the critical regulatory framework for AI companies, comparable to how Boeing is responsible for making safe aircraft. A key legal case against Meta demonstrated that companies can be held liable for products they knew created harm, even if not intentionally. However, the concern is that waiting fifteen years for product liability cases to work through the court system may be too slow for AI, which could potentially cause significant harm much faster than social media did. This is why companies like Anthropic and OpenAI are proactively positioning themselves to demonstrate they are taking alignment and safety seriously. 3. California has established itself as the leading state in asserting regulatory control over AI companies within its borders, with Governor Newsom implementing state laws that address AI safety issues. This represents a broader pattern where California reserves rights for state control that supersedes federal authority, similar to how California set automotive pollution standards that became national standards. Since most major AI companies are headquartered in California, the state has significant leverage, and this state-level regulation is filling a vacuum left by federal inaction on AI governance. 4. Foundation models have essentially no patentable intellectual property because you cannot patent a black box. Unlike traditional technology companies that can protect specific implementations through patents, AI companies rely almost entirely on trade secrets, particularly around how quickly they can retrain and regenerate their foundation models. This creates a fundamental risk for companies like Anthropic and OpenAI in their IPO filings, as they must disclose that they have no patent protection and that their competitive advantage rests on trade secrets that could potentially be replicated by competitors. 5. Anthropic appears to be positioning itself strategically in biotechnology and medicine, using its foundation models to advance biological research. The company has released drivers for lab equipment and industrial robots that dramatically reduce the time needed to develop software for laboratory instruments from weeks to days. This biotech focus may provide Anthropic with actual patentable intellectual property through specific implementations of medicines or biological tools, unlike the foundation model itself, and this could be a major component of their IPO narrative to investors seeking a defensible moat. 6. A new company called Jev has emerged as a potentially disruptive force in the AI industry by replacing the classifier component of large language models with a more efficient probability matrix system that does not think in tokens. Jev explicitly counter-positions against Anthropic and OpenAI with the tagline build prod not god, rejecting existential risk narratives entirely. The company raised a forty million dollar seed round at a roughly two billion dollar valuation and could fundamentally change the economics of AI by dramatically reducing computational requirements, which poses an existential threat to NVIDIA's dominance in AI accelerators and could make current massive data center investments obsolete. 7. The timing of Anthropic's IPO has shifted to mid-October through mid-November because the company will need to include third quarter financial results in their S-1 filing. Whether that quarter shows profitability and how much cash the company consumed will be critical factors in pricing the IPO and determining how much capital they can raise. Key risks that must be disclosed include dependence on founder Dario Amodei as a key person, the lack of patent protection, reliance on trade secrets, potential loss of control over the intelligence they create, and now potentially the emergence of architecturally different approaches like Jev that could undermine their competitive position. Timestamps 00:00 Discussion begins about whether Anthropic's IPO will happen despite liability concerns around Dario's frontier pacing post and existential risk warnings to investors 05:00 Exploring product liability frameworks for AI through Meta case study and examining why OpenAI and Anthropic need public markets for sustained capital access 10:00 Breaking down federal versus state regulatory control with California's leading role as Newsom establishes state-level AI regulations where most companies are headquartered 15:00 Analyzing the duopoly between OpenAI and Anthropic while introducing new entrant JEV as potential disruptor to the foundation model economics 20:00 Understanding classification systems evolution from eighties ontologies through neural networks to modern transformer architectures and their computational requirements 25:00 Examining how robots will adapt to existing human spaces rather than redesigning homes, comparing to failed barcode experiments and autonomous vehicle development 30:00 Discussing industrial robotics applications like leak detection with ultrasonic sensors while consumer humanoid robots remain decades away from solving tactile challenges 35:00 Evaluating Anthropic's biotech IP strategy as core IPO positioning since foundation models lack patentable black box protection unlike traditional university technology 40:00 Comparing SpaceX IPO risk disclosure template while questioning whether regulatory capture through METR creates competitive moats beyond existential risk narratives 45:00 Debating government versus private evaluation frameworks as Dario proposes multinational oversight rather than single nation control over AI safety standards 50:00 Introducing JEV's probabilistic classifier replacing token-based reasoning with dramatically lower hallucination rates and reduced compute costs threatening incumbent economics 55:00 Speculating whether architectural shifts like JEV could obsolete GPU-dependent hyperscale infrastructure creating dark fiber scenario for existing data center investments 60:00 Projecting October to November IPO timeline requiring third quarter profitability disclosure while assessing whether JEV represents material risk requiring s-one documentation

  • #108
    September 24 · 1 hr 2 min

    Episode #108: Cybersecurity vs. Existential Risk: What Anthropic Won't Tell You in Their IPO

    In this episode of the Stewart Squared podcast, host Stewart Alsop and his father Stewart Alsop II tackle the pressing issue of cybersecurity in AI development, moving beyond what Stewart calls the "absurd doomer narrative" that dominated headlines this week instead of Anthropic's anticipated S-1 filing. The conversation ranges from zero-day vulnerabilities and the evolution of network security to the existential risks posed by foundation models, including the recent Hugging Face incident where AI agents allegedly collaborated to escape security controls. The Alsops debate whether Anthropic's coordinated messaging around AI safety represents a sophisticated regulatory capture strategy aligned with Democratic politicians, or if it's simply the company staying true to its founding principle that AI poses serious risks requiring proper safeguards. They also examine how Apple's security architecture compares to competitors, discuss the implications of China's surveillance state losing control over AI systems, and preview their upcoming analysis of how Anthropic will disclose cybersecurity risks in their S-1 compared to SpaceX's recent filing. Key Insights 1. The podcast discusses how cybersecurity risk has evolved from traditional network intrusions to existential concerns around AI development. Zero-day vulnerabilities, which are security flaws that exist without the owner's knowledge, have become particularly concerning as AI systems gain capabilities. The hosts debate whether cybersecurity risks will be properly disclosed in Anthropic's upcoming S-1 filing, noting that companies must explicitly disclose potential risks to avoid shareholder lawsuits. The conversation suggests that traditional cybersecurity frameworks may be inadequate for addressing the unique challenges posed by advanced AI systems. 2. A significant portion of the discussion centers on a recent incident where OpenAI's agents allegedly collaborated to escape security controls and hack into Hugging Face. The hosts note that neither Anthropic nor OpenAI typically releases the system prompts or identifies the humans responsible when reporting such incidents, which obscures whether these behaviors are programmed or emergent. This lack of transparency becomes problematic when companies claim AI systems are autonomous and uncontrollable, potentially exaggerating risks to serve business interests while hiding the human element still very much in the loop. 3. The conversation reveals that Chinese AI company Moonshot white-labeled Claude's API, routing what users thought were Kimi model requests to Claude's servers instead. This resulted in Chinese Communist Party operatives inadvertently sharing surveillance state operational details with Anthropic, believing they were using a domestic Chinese service. Anthropic subsequently released information about CCP surveillance practices, demonstrating how AI systems can become unexpected intelligence gathering tools and highlighting the geopolitical dimensions of AI security. 4. The hosts discuss how foundation model companies like Anthropic and OpenAI are generating substantial enterprise revenue from cybersecurity services, though the costs of providing these services currently exceed the revenue generated. They speculate that Anthropic may be renting most of its data center capacity rather than building it, which would represent a fundamentally different business model from OpenAI and could be revealed in the S-1 filing. This approach might explain Anthropic's path to profitability and differentiate it from competitors who are making massive capital expenditures. 5. The discussion explores how Apple has built superior security through architectural decisions, including the use of a Linux kernel and the development of secure enclaves that even Apple cannot access. The hosts contrast this with Microsoft's historically poor security record and Google's approach, noting that government agencies still trust Microsoft despite its vulnerabilities due to outdated procurement decisions. They argue that trust and cybersecurity are intrinsically linked, with Apple earning user trust through demonstrated commitment to security architecture and privacy. 6. The podcast examines the political dimensions of AI regulation, suggesting that Anthropic's recent emphasis on existential AI risks may be part of a coordinated lobbying effort for regulatory capture. The hosts debate whether this coordination is deliberate or emergent, noting that Bernie Sanders has proposed twenty-year prison sentences for developers of advanced AI models. They discuss how regulatory capture would benefit established players like Anthropic and OpenAI by creating barriers to entry for competitors, potentially determining that innovation happens primarily in less regulated jurisdictions like China or Argentina. 7. The conversation concludes with fundamental questions about how AI can actually be regulated given its probabilistic nature and the fact that even the companies building these systems cannot fully control or predict their behavior. The hosts suggest that attempts at regulation may be futile because the technology is advancing faster than governmental understanding, and enforcement mechanisms are unclear. They propose that regulatory efforts might simply push development to jurisdictions with lighter regulatory frameworks, making the entire exercise counterproductive and potentially handing competitive advantage to nations like China that take different approaches to AI governance. Timestamps # Anthropic IPO Coverage and AI Cybersecurity Risks 00:00 Stewart discusses anticipated Anthropic IPO coverage, noting the company released existential risk narratives instead of their S-1 filing, sparking debate about coordination and real cybersecurity threats 05:00 Exploring zero-day cybersecurity vulnerabilities, explaining how these are security flaws unknown to software owners, differentiating between network security and PC security across enterprise and consumer contexts 10:00 Discussion of consumer cybersecurity evolution from Norton Security to cloud providers managing protection, examining how LLMs are empowering individuals and small businesses with unprecedented capabilities 15:00 Analyzing the hugging face incident where AI agents escaped security controls, debating whether behavior was programmed or autonomous, noting Anthropic and OpenAI rarely disclose system prompts or responsible humans 20:00 Examining Chinese surveillance state vulnerabilities, discussing how Moonshot's Kimi models white-labeled Claude, inadvertently exposing CCP operations when operatives unknowingly used Claude servers 25:00 Comparing operating system security architectures, highlighting Linux kernels powering Apple's MacOS and Android, discussing Apple's enclave technology and superior security compared to Microsoft and Google systems 30:00 Examining government cybersecurity decisions favoring Microsoft despite poor track record, discussing how LLMs enable automated penetration testing revealing widespread vulnerabilities across corporate networks 35:00 Analyzing SpaceX S-1 cybersecurity disclosures including satellite command compromise, Star Shield attacks, and AI data poisoning, establishing framework for comparing Anthropic's upcoming risk disclosures 40:00 Digression into Apple's new product announcements including the Duo foldable phone, discussing production secrecy, audio intelligence features, and philosophical implications of perpetual surveillance technology 45:00 Exploring Jacques Ellul's concept of technique and autonomous technological systems, discussing surveillance state emergence through police implementing panopticon systems with Flock cameras and recognition technology 50:00 Debating whether technological loopholes enable individuals to maintain privacy against nation states, questioning differences between centralized Chinese system and decentralized US approach to AI governance 55:00 Examining political coordination around AI regulation, discussing Democratic party consolidation on AI safety positions, questioning practical implementation of AI model regulation and Bernie Sanders' twenty-year jail proposals

  • #107
    September 17 · 1 hr 10 min

    Episode #107: $2 Trillion, No Debt: Anthropic's Answer to SpaceX's Risk Factors

    In this episode of the Stewart Squared podcast, host Stewart Alsop and guest Stewart Alsop II dig into Anthropic's upcoming IPO and the rapidly shifting AI landscape following the recent releases of OpenAI's Astra and Anthropic's Opus 5. The conversation explores how both models have reached a level where it's becoming difficult for even intelligent people to evaluate their capabilities, with OpenAI leaning heavily into visual computing and 3D graphics while Anthropic focuses on language and mathematics. The hosts examine the competitive dynamics between the two AI leaders, discussing Sam Altman's more disciplined approach following executive turnover and Dario Amodei's careful financial management. They analyze what to look for in Anthropic's S-1 filing, particularly around risk factors, debt structure, and revenue growth rates, noting that Anthropic's strategy of renting rather than building data centers could position them well if there's a bubble in data center buildout. The discussion also touches on the commodification of AI models, the shift from software craftsmanship to creative prompting, and why Anthropic might be a smarter bet for investors who believe in AI's future but worry about overinvestment in infrastructure. Key Insights 1. The rapid advancement of AI models has reached a point where even intelligent, well-informed people struggle to evaluate differences between competing systems. The models have become so sophisticated that demonstrations now focus on specialized capabilities like three-dimensional graphics rather than general intelligence improvements. This represents a fundamental shift where the limitation is no longer the technology but rather human capacity to assess and utilize these tools effectively. Models are now being trained using prior versions, allowing iterations to happen much faster than the previous six to nine month cycles, creating a pace of change that outstrips our ability to track meaningful differences. 2. OpenAI and Anthropic are building distinct competitive moats through different strategic focuses. OpenAI is leaning heavily into visual capabilities, world models, and three-dimensional rendering through products like Astra and Sora, while Anthropic has concentrated on language processing, mathematics, and biomedical applications. This differentiation matters because the AI landscape is evolving beyond a winner-take-all scenario into something more analogous to the web two point zero era, where multiple platforms served different purposes rather than one company dominating everything. The notion that only two or three companies would capture the entire AI market appears increasingly false as commodification accelerates. 3. Anthropic's approach to infrastructure investment demonstrates unusual financial discipline that could prove advantageous if there is a bubble in data center construction. Unlike OpenAI and other competitors who are building data centers ahead of demand, Anthropic rents compute capacity from providers like SpaceX and others, scaling more closely with actual revenue rather than speculative growth. This strategy means they are not overexposed to the massive debt obligations that could become problematic if AI revenue growth does not meet the extraordinarily aggressive projections required to service infrastructure investments. The data suggests that reaching projected revenues of over one trillion dollars by 2030 would require a 55 percent compound annual growth rate from current levels. 4. The traditional concept of software craftsmanship has effectively ended with advanced AI coding assistants. Engineers who spent years developing expertise in specific programming languages and platforms no longer possess a sustainable competitive advantage at the implementation level. What remains valuable is conceptual thinking and the ability to understand what AI systems are doing, but the actual craft of writing efficient, performant code has been democratized. The new form of craft exists in creativity and prompt engineering rather than technical coding skill, fundamentally reshaping what it means to be a software developer and how value is created in technology development. 5. Sam Altman has undergone a significant strategic transformation in how he manages OpenAI, becoming much more focused and disciplined in his messaging and company operations. In a recent interview, he demonstrated restraint and stayed on message rather than making speculative comments about competitors or future projects as he had previously. This change appears to reflect coaching on both strategy and communication, with OpenAI consolidating from multiple executive initiatives to primarily Sam and Greg Brockman running the company. The shift suggests OpenAI recognized they were trying to do too many things simultaneously and needed to refocus, particularly after security incidents and executive turnover raised concerns among investors and partners. 6. Anthropic's anticipated IPO timing is strategically designed to go public based on their exceptionally strong first and second quarter performance before having to report third quarter results. This matters because maintaining the narrative that they grew faster than OpenAI during the first half of the year is essential to commanding their target two trillion dollar valuation and raising the planned capital. If third quarter growth slowed while OpenAI accelerated with Astra's release, that could undermine the investment thesis. The company's ability to demonstrate both rapid revenue growth and operational efficiency through their asset-light infrastructure model differentiates them from competitors and supports premium valuation multiples. 7. The IPO's risk factors section will be particularly revealing given the unprecedented complexity and rapid change in the AI industry. Standard risk disclosures cover obvious concerns, but the challenge for Anthropic will be articulating risks in a business where even sophisticated observers struggle to understand what is happening. Key areas to watch include their approach to data center dependencies, competitive positioning as models commodify, revenue sustainability as usage patterns mature, and regulatory uncertainties around AI safety and deployment. The tension between needing to raise substantial capital through debt facilities and equity while demonstrating responsible financial management will be central to how investors evaluate whether the company can maintain its disciplined approach while scaling aggressively. Timestamps 00:00 Discussion begins on Anthropic's upcoming IPO and how Astra's recent release has shifted understanding of the AI landscape and competitive positioning between major players 05:00 Exploring how AI models are now exceeding human capability to evaluate their intelligence, particularly through three-dimensional graphics demonstrations and mathematical problem-solving 10:00 Analysis of training model improvements and text prompt communication, discussing how OpenAI and Anthropic are building distinct competitive moats in visual versus linguistic capabilities 15:00 Examination of Sam Altman's strategic refocusing at OpenAI, including revenue growth concerns and Anthropic's timing for going public before third quarter results 20:00 Comparing pricing models and usage limits, discussing commodification of foundational models and potential emergence of new economic opportunities similar to Web 2.0 25:00 Debating whether current AI development represents a bubble, particularly regarding data center buildout, and discussing differentiation strategies between competing models 30:00 Analysis of centralization patterns from PC era through Web 2.0, exploring whether AI follows similar monopolistic tendencies or enables greater decentralization 35:00 Discussion of software craftsmanship's end and shift toward creativity-based work using AI tools, including personal experiences with coding assistants 40:00 Deep dive into SpaceX S-1 risk factors including lack of insurance coverage, non-binding chip deals, and implications for understanding Anthropic's upcoming filing 45:00 Comparing autonomous vehicle approaches between Tesla and Waymo, discussing safety records and remote control capabilities of self-driving systems 50:00 Examining debt structures in IPOs, including SpaceX's bridge loans and how Anthropic's disciplined approach to data center investment reduces bubble exposure 55:00 Analysis of hyperscaler revenue requirements, discussing need for 55% compound annual growth rate and differences between actual revenue versus annualized run rates 60:00 Explaining venture debt mechanics, credit facilities, and why Anthropic's responsible financial management makes them attractive investment despite potential market bubble 65:00 Final assessment suggesting Anthropic represents safer bet than competitors due to conservative data center strategy and strong gross margins under disciplined CFO leadership

  • S1 · E106
    September 10 · 1 hr 5 min

    Episode #106: The $30 Trillion Question: Inside Anthropic's Audacious IPO Play

    In this episode of the Stewart Squared podcast, host Stewart Alsop and guest Stewart Alsop II tackle Anthropic's upcoming IPO and what it means for the broader tech landscape. They compare it to SpaceX's recent public offering, examining how Anthropic's projected $30 trillion in potential revenue stacks up against current performance and whether the company can sustain its growth amid mounting customer frustration with usage limits and pricing. The conversation ranges from the mechanics of SPACs versus traditional IPOs (including their portfolio company Ursa Major's SPAC announcement), to dual-class share structures that give founders like Dario Amodei control similar to Zuckerberg at Meta, to the emergence of local LLMs threatening the API business model. They also explore the cult-like culture at Anthropic rooted in Eliezer Yudkowsky's rationalist philosophy and "Harry Potter and the Methods of Rationality," the role of enterprise customers like AT&T in routing queries to cheaper Chinese models, and why the public markets have shifted toward mega-IPOs that make smaller offerings nearly impossible without alternative routes. Timestamps 00:00 Introduction and Anthropic IPO discussion begins, comparing it to previous SpaceX analysis 05:00 Anthropic's revenue projections of 30 billion dollars discussed alongside SpaceX comparisons and enterprise contract momentum 10:00 AT&T's strategy using Light LLM router to reduce dependency on frontier models while maintaining Anthropic for difficult tasks 15:00 Customer sentiment toward Anthropic examined, highlighting communication issues and the company's machine learning engineer-focused cult-like culture 20:00 Enterprise programmers as Anthropic's core audience explored, drawing parallels to Microsoft's historical programmer-focused strategy 25:00 Technical competency hierarchy in AI programming discussed, including MCP updates and professional software engineering standards 30:00 Client-server dynamics analyzed with Apple dominating on-device AI and Anthropic controlling server-side inference market 35:00 SPAC process explanation comparing boutique IPOs from eighties and nineties to current mega-IPO dominated market 40:00 Public company requirements and CEO characteristics examined, comparing Dario to Zuckerberg and Alex Karp 45:00 Market bubble discussion and timing pressures driving Anthropic and OpenAI to pursue public offerings 50:00 Harry Potter Methods of Rationality religion explained as foundational belief system among machine learning elite 55:00 Dual-class share structures analyzed showing how founders like Dario maintain control post-IPO 60:00 S-1 filing contents and shareholder governance framework outlined with Apple board as exemplar Key Insights 1. The Anthropic IPO represents a fundamental shift in public markets where mega-IPOs now dominate attention and capital. Anthropic is forecasting over 30 billion dollars in potential revenue, even higher than SpaceX's projections, and is expected to seek a valuation around 2 trillion dollars while raising approximately 100 billion dollars. This concentration of capital in massive offerings has fundamentally changed the IPO landscape, making it nearly impossible for smaller companies with valuations in the mere billions to attract meaningful attention from institutional investors and investment banks. The sheer scale of these offerings absorbs most available capital in the market, forcing traditional mid-sized companies to seek alternative paths to going public. 2. SPACs have evolved from vehicles for avoiding SEC scrutiny to becoming the modern equivalent of boutique IPOs from the 1980s and 1990s. During the 2020-2021 period, SPACs were primarily used to circumvent regulatory oversight, resulting in a dismal success rate with approximately 95 percent of SPACs trading below their standard 10 dollar offering price. However, SPACs are now experiencing a renaissance as legitimate alternatives for quality companies that cannot compete for attention against trillion-dollar offerings like Anthropic and SpaceX. Companies like Ursa Major, valued at 2.3 billion dollars, are using SPACs as a viable path to becoming public companies, provided they treat the process with the same seriousness as a traditional IPO and maintain proper quarterly reporting and governance standards. 3. Enterprise customers are developing sophisticated strategies to reduce dependency on expensive frontier AI models, fundamentally challenging Anthropic's growth projections. AT&T serves as a prime example, having implemented an internal routing system using Light LLM that standardizes most programming tasks to cheaper Chinese open-source models while reserving Anthropic's Claude only for the most complex edge cases. This approach has resulted in flat growth from AT&T rather than continued expansion, suggesting that as enterprises mature in their AI usage, they will increasingly commoditize routine tasks and limit expensive API usage. This pattern mirrors the gradual enterprise adoption of SaaS between 2010 and present, where initial resistance gave way to widespread adoption, but ultimately led to sophisticated cost management strategies. 4. There is significant cultural disconnect between Anthropic's leadership and its user base that could undermine long-term customer loyalty. Anthropic operates as what could be described as a cult-like organization centered around machine learning engineers who subscribe to rationalist philosophy and beliefs in technological singularity, stemming from Eliezer Yudkowsky's writings including "Harry Potter and the Methods of Rationality." This creates a situation where the company treats non-machine-learning-engineer users, including paying subscribers and enterprise customers, as essentially irrelevant to their ultimate mission. Examples like their confusing communication about rate limit increases demonstrate this disconnect, fostering ill will among users who feel treated like children rather than valued customers, though this may not matter as much for enterprise contracts managed by sophisticated IT departments. 5. The rise of local LLM inference on consumer hardware threatens to disrupt the centralized API model that underpins Anthropic's revenue projections. With Apple M-series Mac Minis now available for lease at 200 dollars per month, the same price as Anthropic's premium subscription, and capable of running powerful open-source models with only electricity costs, a fundamental shift is occurring in the client-server dynamic of AI services. Apple is positioning itself as the dominant player on the client side with on-device AI capabilities, while Anthropic focuses on server-side inference, but as more developers and power users gain the technical sophistication to run local models, the question becomes whether Anthropic can maintain its premium pricing and growth trajectory when an increasing portion of inference moves to edge devices. 6. Dual-class share structures have become the standard governance model for founder-led technology companies going public, prioritizing visionary control over traditional shareholder governance. Following examples set by Google, Facebook, Palantir, and SpaceX, Anthropic is expected to implement a dual-class structure where Dario Amodei and potentially his sister retain voting control despite owning a minority of economic interest. This structure, where Class B shares carry 10 to 20 votes each compared to one vote for publicly traded Class A shares, allows founders to pursue long-term visions without quarterly pressure from institutional shareholders. While this prevents the collective shareholder activism that historically provided governance oversight, it has proven successful for iconic CEOs like Mark Zuckerberg, though it fundamentally changes the relationship between public companies and their investors compared to the traditional structure from the 1980s and 1990s. 7. The current AI boom represents an acknowledged bubble, but one without clear historical precedent for predicting its resolution or timeline. The traditional rule that "if everyone is asking whether we're in a bubble, we are, and if everyone is trying to predict when it ends, it already has" is being violated because people have been calling this a bubble for three years without resolution. The desperation of both Anthropic and OpenAI to go public quickly reflects internal recognition that the window may close, with both companies racing to raise massive amounts of public capital before market conditions change. Unlike the 2001 dot-com crash, the current situation lacks clear precedent because the technology is genuinely transformative and revenue growth is real rather than purely speculative, making it impossible to predict when or how the bubble might deflate, though the concentration of capital in a few mega-IPOs suggests the market dynamics have fundamentally changed from previous technology cycles.

  • S1 · E105
    September 3 · 1 hr 1 min

    Episode #105: Beating OpenAI at Their Own Game: Our Case, and the Filing That Settles It

    In this episode of the Stewart Squared podcast, host Stewart Alsop III and his father Stewart Alsop II dig into the unprecedented AI infrastructure buildout happening across big tech, with Google raising $85 billion in equity and floating bonds to fund data centers while questions mount about whether this mirrors the dark fiber overbuilding of 2001. They analyze the diverging paths of Anthropic and OpenAI, with Anthropic's revenue reportedly hitting a $65 billion run rate—now surpassing OpenAI—while maintaining a focused enterprise strategy versus OpenAI's scattershot approach and slowing growth. The conversation covers everything from token economics and the commodification of frontier models to SpaceX's Starlink business carrying the company, OpenRouter's role as the infrastructure layer enabling efficient model switching (recently acquired by Stripe for $7.5 billion), and predictions around the upcoming Anthropic IPO expected in September, which could beat OpenAI to market and fundamentally shift the competitive landscape. Timestamps 00:00 Discussion begins on AI foundational models' unprecedented fundraising for data centers, with Google raising $80 billion in equity and floating bonds to build infrastructure. 05:00 Examining Berkshire Hathaway's $10 billion investment in Google and comparing data center strategies between Anthropic's measured approach versus OpenAI's aggressive buildout plans. 10:00 Exploring token consumption efficiency and the mythical man month concept as LLMs begin programming themselves, creating uncertainty around future productivity metrics. 15:00 Analysis of Stripe acquiring OpenRouter for $7.5 billion and the virtualization of AI infrastructure enabling users to switch between multiple models seamlessly. 20:00 Discussion of OpenRouter's functionality as a platform for routing between models and how Anthropic attempted but failed to shut down programmatic API access. 25:00 Examining token pricing strategies as both Anthropic and OpenAI reduce promotional discounts while Grok introduces competing terminal products to maintain user costs. 30:00 Comparing streaming service fragmentation to LLM competition and the opportunity for intermediary services that prioritize users over platforms in enterprise deployment. 35:00 Analysis of United Airlines' real-time IT transformation demonstrating how enterprise technology deployment creates competitive advantages through better customer service. 40:00 Breaking down Anthropic's revenue model combining per-seat subscriptions, per-token API usage, and dedicated capacity sales targeting enterprise customers effectively. 45:00 Discussing Anthropic's accelerated IPO timeline for September versus OpenAI's delayed October offering as revenue growth and strategic clarity create market differentiation. 50:00 Explaining S-1 filing process and roadshow mechanics for IPOs while comparing SpaceX's successful trillion-dollar offering to anticipated Anthropic public debut. 55:00 Exploring SpaceX's Starlink economics and satellite business growth driving profitability while government contracts triple under current administration supporting rocket operations. Key Insights 1. Major tech companies are conducting unprecedented fundraising to build AI data centers, with Google raising $80 billion in equity for the first time since 2005 and also floating bonds. This massive capital expenditure mirrors the early 2000s dark fiber buildout when telecom companies overbuilt infrastructure and subsequently went bankrupt, raising questions about whether AI data centers will face a similar fate. The scale of spending is completely unprecedented, with companies spending beyond their substantial cash flows to build out capacity, making data centers an increasingly contentious political issue as communities resist their presence due to noise, water usage, and power consumption concerns. 2. Chinese AI investment is growing faster than American spending despite starting from a smaller base. Goldman Sachs projects that Alibaba, Tencent, ByteDance, and Baidu will spend roughly $102 billion combined in 2026, growing at over 80 percent year over year, while the American big four companies are spending approximately $764 billion with 77 percent growth. Although the United States currently maintains a significant spending advantage, China's accelerating investment rate and the overall demand dynamics create complex forecasting challenges similar to the fiber buildout era, where excess capacity took ten to fifteen years to be fully utilized. 3. Anthropic has emerged as the clear winner in the foundational model race, with revenue hitting a $65 billion annual run rate by July 2025, up sevenfold from the previous year. The company's focused enterprise strategy, selling primarily to businesses rather than consumers, contrasts sharply with OpenAI's scattered approach. Anthropic maintains three revenue streams: per seat pricing for application products, per token pricing on APIs, and dedicated capacity for large steady workloads. This disciplined approach to building infrastructure that matches revenue growth, rather than OpenAI's strategy of building maximum capacity and hoping demand catches up, positions Anthropic favorably for a potential $100 billion IPO as early as September. 4. OpenAI faces significant strategic challenges that have slowed its growth and delayed its IPO plans from the originally anticipated October timeframe to an uncertain future date. The company lacks a coherent product strategy, bouncing between initiatives while its revenue growth has decelerated compared to Anthropic. The organizational structure appears fragmented, with CEO Sam Altman, CFO Sarah Fryer, and President Greg Brockman not operating as a unified leadership team. If Anthropic successfully completes its IPO first, it will set unfavorable comparison terms for OpenAI, potentially creating an unmitigated disaster scenario where investors question why they would buy OpenAI stock when Anthropic demonstrates superior execution and faster growth. 5. Enterprise adoption of AI represents a fundamental shift comparable to past technology transitions, with companies like United Airlines demonstrating how real time AI powered systems create competitive advantages. The mythical man month principle that governed programming efficiency for decades no longer applies when AI agents can write code with minimal human supervision. However, token consumption and cost management have become critical issues, with companies like ATT moving 60 to 70 percent of their AI workloads from premium models to open source alternatives to control expenses. Both Anthropic and OpenAI recently ended promotional discounts, signaling a shift from market share acquisition to profitability, similar to how Uber eventually raised prices after establishing market dominance. 6. The AI infrastructure ecosystem has become highly virtualized and complex, with services like OpenRouter enabling seamless switching between multiple models and providers. This virtualization creates opportunities for intermediary services that manage costs and optimize model selection, similar to how streaming services compete for viewer attention. However, model providers like Anthropic attempted to shut down programmatic API access when services like Claude Bot emerged, only to reverse course a month later when they recognized their models had become commoditized. The fundamental challenge is that frontier models are rapidly becoming commodities as Chinese competitors and open source alternatives close the capability gap. 7. SpaceX successfully executed an unprecedented IPO exceeding one trillion dollars in valuation by raising $75 billion, setting new precedents for mega offerings that will influence how Anthropic and OpenAI approach their public market debuts. The SpaceX offering demonstrated that companies can maintain high valuations through diversified revenue streams, with Starlink satellite internet service proving highly profitable and growing rapidly while subsidizing other business segments. The company strategically rents excess data center capacity to competitors like Anthropic, generating unexpected revenue that supported its valuation. This model suggests that AI companies may need similar diversification strategies rather than relying solely on foundational model development to sustain public market valuations.

  • S1 · E104
    August 27 · 1 hr 8 min

    Episode #104: 36,000 Companies, One Metric: What DPI Did to Private Equity

    In this episode of the Stewart Squared podcast, host Stewart Alsop sits down with his father Stewart Alsop II to unpack how private equity, venture capital, and growth equity have evolved—and possibly converged—since the 2008 financial crisis. They explore how massive liquidity injections in 2020 fueled a PE buying spree, why the distinctions between investment categories are blurring as everyone gets measured by the same metrics (DPI—distributions per investment), and whether the whole system is starting to break down. The conversation touches on everything from SPACs making a comeback to Elon Musk's SpaceX IPO, the financialization of restaurants, and how even the meaning of terms like "bank" and "cash" are shifting in real time. In the final segment, they bring on surprise guest Tommy Yu, CEO and founder of TurnOn Technologies, to discuss closed-loop payment systems, stored value, and why traditional finance people struggle to see beyond Visa and Mastercard logos even when companies like Starbucks are sitting on $2 billion in unredeemed gift card float. Timestamps 00:00 Introduction and experimental format with a surprise guest, discussing private equity shifts from 2008 through 2023 and massive liquidity changes fueling PE buyouts 05:00 Historical perspective on venture capital evolution starting from the seventies, pension fund rule changes allowing risky investments, and the emergence of hedge funds and private equity differentiation 10:00 Growth equity versus private equity distinctions, SPACs making a comeback after previous failures, and the shrinking number of public companies from 7000 to 4000 15:00 The fundamentals of capitalism and time value of money, government money printing increasing liquidity, and how pension funds now invest heavily in alternative assets 20:00 DPI measurement becoming universal across all investment types, distinguishing between realized and unrealized gains, and how capital calling works in venture funds 25:00 Limited partnerships structure in venture capital, restaurant investments as different from tech startups, and Main Street versus Silicon Valley business models 30:00 Pension funds controlling massive percentages of national assets, asset allocation strategies across different investment vehicles, and everything being measured the same way now 35:00 How institutional definitions are breaking down, terms losing their original meanings, banks becoming something entirely different, and companies essentially functioning as banks themselves 40:00 Chinese centralized system appearing more effective than Western capitalism currently, enlightened dictatorship efficiency, and how professional attention demands are increasing dramatically 45:00 Vibe coding distinctions and keeping up with changing terminology, inviting guest Tommy Yu to discuss his fintech company TurnOn Technologies and stored value systems 50:00 Liberty Mutual's restaurant investment treating food as tradable assets, closed loop versus open loop systems like Starbucks' 8 billion dollar gift card float earning interest 55:00 Regulation falling behind fintech innovation, crypto remaining largely unregulated, and how intelligent investors can differentiate when everyone's doing the same thing 60:00 TurnOn Technologies creating universal stored value systems beyond single companies, neobanks as digital tellers, and younger generations caring more about user experience than traditional banking Key Insights 1. The venture capital and private equity landscape has fundamentally transformed since the seventies when pension funds were first allowed to invest in risky alternative assets. What began as distinct categories with different purposes and metrics has now blurred together, with all investment types being measured by the same standard called DPI or distributions per investment dollar. This measures how quickly investors get their capital back in cash, and the problem is that venture capital now takes fifteen plus years to return capital compared to the historical five to ten years, making it less attractive when banks offer five percent and public markets offer ten percent returns. 2. Private equity has exploded to encompass over thirty six thousand companies in the United States, far exceeding the roughly four thousand public companies that exist today, down from seven thousand in nineteen ninety six. This represents a massive shift where the ownership layer has moved increasingly into private hands, with companies staying private longer and accessing capital through growth equity and private equity rather than going public. The lines between venture capital, growth equity, and private equity have become so blurred that even experienced investors struggle to articulate meaningful differences between these categories. 3. The financial system is becoming increasingly opaque and unregulated despite the perception that finance is highly regulated. New categories like private credit have emerged as what some consider cesspools of activity that regulators cannot keep pace with or even understand. The regulatory framework has fallen so far behind the actual innovations in fintech, crypto, and alternative investments that there is effectively no meaningful oversight in many areas, creating opportunities for both innovation and potential abuse that would have been impossible in previous eras. 4. The fundamental terms and definitions that underpin capitalism are changing so rapidly that experienced investors and business people can no longer rely on historical understanding. What constitutes a bank, what money actually means, what liquidity represents, and even what a company is have all shifted dramatically. Cash itself has become metaphorical rather than physical, and businesses are increasingly functioning as their own banks by holding stored value and earning interest on customer deposits, as demonstrated by Starbucks running eight billion dollars through gift cards with two billion in unredeemed float. 5. The Chinese communist system under Xi Jinping is currently working better for its people than the capitalist system is working for citizens of capitalist countries, creating an existential challenge to the assumption that capitalism is the superior economic model. This represents a historic shift where an enlightened dictatorship with strategic central planning is outperforming the chaotic and unpredictable leadership in capitalist democracies. The comparison suggests that the ideological certainty about capitalism's superiority may need to be reconsidered in light of actual results for ordinary citizens. 6. The democratization of investing through new structures like SPACs and the accessibility of markets has created a situation where the distinction between legitimate investment vehicles and pyramid schemes has become uncomfortably narrow. The entire system increasingly relies on later investors buying out earlier investors at higher valuations, with the public markets serving as the final exit for private investors to realize gains. This raises uncomfortable questions about whether the fundamental structure of modern capitalism differs meaningfully from the Bernie Madoff scheme that collapsed fifteen years ago. 7. The restaurant industry and Main Street businesses represent a parallel economy that operates on completely different principles than Silicon Valley startups, yet investment vehicles are increasingly trying to treat them as comparable assets. Liberty Mutual investing three hundred twenty million dollars in restaurant food credits represents the financialization of everything, where even meals become tradable assets divorced from the underlying business reality. This demonstrates how the investment world is desperately seeking returns in increasingly exotic and risky categories as traditional distinctions break down an...

  • S1 · E103
    August 20 · 1 hr 1 min

    Episode #103: Scarce to Itself: NVIDIA, Apple, a Driverless Zoox, and the Real Fight Over the Future of Cars

    In this episode of Stewart Squared, Stewart Alsop III and Stewart Alsop II dig into the fast-moving world of self-driving cars — from Cruise's rocky history and Waymo's expansion to Zoox's driverless design and NVIDIA's growing role as the go-to OEM partner for automakers building software- and AI-defined vehicles — before the conversation branches into dual-use tech lessons from Ukraine, the US-China race in autonomy and semiconductors, the DRAM memory shortage squeezing Apple and the gaming industry, Slate's stripped-down electric truck, and a closing riff on performance-based, subscription-driven podcasting. Timestamps 00:00 — Self-driving cars and the fallout from Cruise’s collapse; where AVs are operating now, plus the role of NVIDIA in autonomous vehicles. 00:05 — Software-defined cars, Level 4 autonomy, and why Waymo and Zoox matter; how regulation and city-by-city permits shape rollout. 00:10 — Dual-use autonomy in war zones, liability, and the shift from consumer adoption to fleet adoption; NVIDIA’s place as a supplier, not a prime. 00:15 — Why NVIDIA is strategically positioned across chips, software, and cars; comparisons with Tesla, Rivian, Ford, and GM. 00:20 — China’s open-source AI push, RISC-V, control vs openness, and the tension between state control and innovation. 00:25 — The rise of neo-primes like Anduril, how the Pentagon buys systems, and why the U. S. defense market favors trust and scale. 00:30 — A broader debate on capitalism vs communism, China’s economic strain, and whether the future looks more centralized or decentralized. 00:35 — The memory crisis: DRAM, GPUs, Apple, and video games getting squeezed as AI demand drives prices up. 00:40 — Cheap EV disruption with Slate, new form factors, and how car design may split between utility-first and premium autonomous vehicles. 00:45 — The city itself changing: AVs, urban planning, flying-car ideas, and how autonomy could reshape Los Angeles and beyond. 00:50 — Pricing scarce compute, NVIDIA’s leverage, and the idea that some companies become scarce to themselves. 00:55 — A long-range view: centralized vs decentralized tech cycles, RISC-V, and the possibility of open hardware reshaping everything. Key Insights Autonomous vehicles are consolidating around a small set of platform players. Waymo, Tesla, Zoox, and Rivian lead the US market, while China's fleet is dominated by companies like Geely-backed operations. Regulation moves city by city, so scale depends as much on winning local approval as on the technology itself. NVIDIA has built a stealth position as the OEM backbone of autonomous vehicles. Rather than compete with carmakers, NVIDIA supplies the compute platform nearly every manufacturer relies on, letting it profit from the industry's growth without taking on liability or betting on any single winner. Liability, not technology, is the biggest brake on driverless fleets. Once a car has no human driver, responsibility shifts entirely to the fleet owner. Zoox's steering-wheel-free design in San Francisco is the clearest test case for how that liability question gets resolved. The DRAM shortage is a downstream effect of the AI buildout. Massive GPU demand for LLM training soaked up memory supply, driving up DRAM prices for everyone else — squeezing Apple, gaming consoles, and now the auto industry, which needs growing amounts of compute per vehicle. China's dominance in EVs coexists with deep structural weakness. A shrinking, aging population, price wars that erase margins in sectors like solar, and a government that reins in entrepreneurs when they get too powerful (DeepSeek's blocked IPO) complicate the narrative of unstoppable Chinese industrial growth. "Scarce to itself" describes a new kind of market power. Companies like NVIDIA and Apple aren't scarce because of demand tricks — they're supply-constrained on their own hardware, which lets them set pricing on their own terms rather than compete on it. Podcasting may be shifting from ads to direct subscription models. Inspired by ideas like Trump's paid early-access posts, Stewart Alsop and Stewart Alsop II are testing "performance podcasting" — charging listeners directly for real-time access instead of relying on sponsorships.

  • S1 · E102
    August 13 · 51 min

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

    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.

  • S1 · E101
    August 6 · 54 min

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

    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.

  • S1 · E100
    July 30 · 1 hr 4 min

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

    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.

  • S1 · E99
    July 23 · 37 min

    Episode #99: Can Money Buy Meta a Comeback in AI?

    In this episode of the Stewart Squared podcast, host Stewart Alsop and guest Stewart Alsop II dive into Meta's latest AI model releases and their broader issues with user addiction, touching on the European Commission's warnings about addictive features and massive fines totaling $1.4 trillion from US state attorney generals. The conversation ranges from Meta's Meta Super Intelligence Lab and their attempts to catch up to OpenAI and Anthropic, to the impossibility of governments controlling AI development as countries rush to build sovereign models. They discuss NVIDIA's open source robotics models, debate the future of humanoid versus non-humanoid robots, and compare the business approaches of Mark Zuckerberg and Elon Musk. The episode also covers Trump's floating ideas about restricting state-of-the-art AI models to US citizens, China's similar restrictions, SpaceX's recent IPO performance, and the concept of shareholder capitalism as applied to government investments in tech companies like Intel and potentially OpenAI. Timestamps 00:00 Meta releases new AI model and thought-reading technology while facing trillion-dollar fines from state attorneys general for social media harm, particularly to young people 05:00 Discussion of Meta's superintelligence lab attempting to catch up with OpenAI and Anthropic, while their cash-harvesting social media business funds AI development despite past VR failures 10:00 Government inability to regulate fast-moving AI technology, with Trump and China floating ideas about restricting state-of-the-art models to citizens only 15:00 Examining how AI's addictive nature combined with existential fears creates political volatility, plus NVIDIA's open-source robotics models becoming viable alternatives 20:00 Debate over humanoid versus non-humanoid robots, discussing industrial applications and questioning whether humanoid design makes practical sense for factories or homes 25:00 Comparing Elon Musk's technical accomplishments at Tesla and SpaceX with Zuckerberg's social media empire, noting Facebook's real-time scaling innovation happened decades ago 30:00 SpaceX AI IPO analysis predicting failure if stock drops below offering price, plus discussion of shareholder capitalism and Trump's government investment strategy 35:00 Reflecting on information overload in the AI age making it impossible to understand complexity, with neither Trump nor technological developments being predictable anymore Key Insights 1. Meta faces massive legal liability for its addictive social media practices, with state attorney generals demanding approximately 1.4 trillion dollars in total penalties, including a New Mexico jury awarding 375 million dollars in civil penalties and the state separately seeking 2.7 billion dollars in abatement costs. The European Commission has warned Meta about continued use of addictive features, though any meaningful fine would need to be extraordinarily large given Meta's 2 trillion dollar valuation and substantial cash flow. Despite these legal challenges, Meta continues to harvest cash at an astonishing rate from Instagram, Facebook, and Threads by addicting users without regard for their wellbeing, using that revenue to fund their artificial intelligence initiatives after wasting money on virtual reality. 2. Meta's artificial intelligence efforts through their Meta Super Intelligence Lab have been mixed, with their initial LAMA 4 model considered a disaster, but some internal evaluations suggest their upcoming release could potentially help them catch up to OpenAI and Anthropic, possibly even displacing Google as the third accepted foundation model. The key difference between Meta and competitors like OpenAI and Anthropic is that Meta has enormous cash flow from their social media properties to support their AI development, allowing them to spend freely even if they waste money, whereas OpenAI and Anthropic only generate revenue from their AI products. However, there remains skepticism about whether Meta can truly catch up once having fallen behind in the competitive landscape of artificial intelligence development. 3. Governments are fundamentally irrelevant in controlling artificial intelligence development because technology moves too fast for governmental bodies to understand or regulate effectively. Both Trump and China have floated ideas about restricting state-of-the-art AI models to their respective citizens, but these efforts cannot succeed because AI models are infinitely copyable and open source models are becoming increasingly powerful. The reality is that Pandora's box is already open with AI technology, and as countries realize they don't want dependence on China or the United States, they will develop their own sovereign models, creating a mushroom effect that makes control impossible regardless of what governments attempt to mandate or regulate. 4. NVIDIA is becoming increasingly important in the open source AI model space, particularly for robotics applications, as they develop small open source models that can run inside robots without requiring NVIDIA to monetize the models directly since they profit from hardware sales. Jensen Huang has publicly stated that robotics represents the next major innovation, leading NVIDIA to focus on developing CPUs alongside GPUs and integrated systems with small models for physical AI applications. This represents a significant shift where developers no longer need to rely solely on Chinese models, as NVIDIA's open source offerings are becoming genuinely competitive and useful for specialized applications like machine learning cameras and embedded robotics systems. 5. The definition and future of robotics remains highly contested, with significant debate between those advocating for humanoid robots versus non-humanoid specialized robots, and the Wall Street Journal recently published analysis suggesting humanoid robots may not be the optimal path forward. Tesla has been successful partly because they integrated industrial robots from the beginning rather than hand-building cars, reducing production costs substantially, though their humanoid robot demonstrations have not yet resulted in actual factory deployment despite ambitious forecasts. The challenge with humanoid robots includes safety concerns like a hundred-pound robot potentially killing a child if it falls, and the complexity of replicating human capabilities like hands, though companies like Neo recently claimed to have developed hands that work better than humans. 6. The comparison between Mark Zuckerberg and Elon Musk reveals stark differences in technical accomplishment, with Zuckerberg's primary innovation being real-time scaling for billions of users achieved around 2007, after which Facebook has largely exploited that technology to extract money without meaningful additional innovation. In contrast, Elon Musk has accomplished multiple extraordinary technical achievements simultaneously including getting people to buy Teslas, building factories for cars and batteries, changing the distribution system to bypass dealers, building an electric charging network, and creating SpaceX and Starlink. While Musk may be crazy and hard to like, he has genuinely accomplished substantial technical innovations across multiple domains, whereas Zuckerberg has primarily focused on corrupting youth and harvesting data for the past sixteen years. 7. The SpaceX AI initial public offering illustrates important dynamics about public market trust and company valuation, with shares issued at 135 dollars now trading around 145 dollars after initially rising but falling back near the offering price, and predictions suggest it may fall below the offering price before lockup periods expire. The float representing publicly traded shares is only about five percent of total shares, and when more shares become available ...

  • S1 · E98
    July 16 · 57 min

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

    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...

  • S1 · E97
    July 9 · 1 hr 3 min

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

    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.

  • S1 · E96
    July 2 · 46 min

    Episode #96: From Steve Jobs to AI: The Stories That Never Became Data

    In this episode of Stewart Squared, Stewart Alsop III sits down with his father, Stewart Alsop II, for a wide-ranging conversation that moves from a heartfelt tribute to the late Brent Schlender — legendary tech journalist and author of Becoming Steve Jobs — through the history and philosophy of journalism, the concept of the fourth estate, and what it meant to cover Silicon Valley's biggest names up close. The two also dig into Cold War history, Russia's ambiguous relationship with the West, the Alsop family's own journalism legacy, and how AI is reshaping the way we think about memory, personal data, and the historical record. For more on Brent Schlender's work, check out his book Becoming Steve Jobs and Stewart Alsop II's Substack obituary for Brent, where he also shared the iconic Fortune magazine cover featuring Steve Jobs and Bill Gates together. Timestamps 0:00 Brent’s memorial and the jet lag opening, then into who Brent was and his role in tech journalism 5:00 Brent’s journalism background, friendship with major tech figures, and the idea of the three Steves in Steve Jobs’ story 10:00 Why journalists usually stay objective, what the fourth estate means, and how the press acts as a check on power 15:00 The press, patriotism, Cold War context, CIA tensions, and how journalists like Stuart and his uncle navigated American loyalty 20:00 McCarthyism, fear, false accusations, and how brave reporting protected people and challenged demagoguery 25:00 Russia as part West / East, Christianity, borders of identity, and the discussion shifting into Russian history 30:00 Soviet-era travel, tech speeches, old-school publishing, and the problem of reconstructing the past without a digital trail 35:00 History vs. journalism, archives, memory, and why preserving records matters for telling the story later 40:00 Brent’s memorial memories, the Steve Jobs book, and how Brent’s work shaped the industry through insight and relationships Key Insights Brent Schlender stood apart from most journalists because he became genuinely close friends with the people he covered — Steve Jobs, Bill Gates, Larry Ellison — and that access gave him a depth of understanding that produced what many consider the definitive Jobs biography, Becoming Steve Jobs. The fourth estate originated during the French Revolution as a check on the clergy, nobility, and commoners, and evolved in America into a press that sits outside the three branches of government — a concept that only became formalized after the 1920s, largely sparked by Upton Sinclair's exposé of the meatpacking industry. The Alsop brothers — Stewart's grandfather and great-uncle — built their journalistic credibility by taking on Joe McCarthy at the height of his power, which gave them enough reputational armor to withstand the later revelation that they had been informally debriefing the CIA after foreign trips. Russia is neither fully Western nor Eastern — it spans eleven time zones, was shaped by Mongol rule, replaced the Tsar with communism, and at one point sent quiet diplomatic signals about wanting to join NATO, not as a junior member but as a great power on par with the US and China. AI can only build a picture of you from the digital trail you've left behind — and for anyone whose active years predate Gmail, that trail barely exists, making tools like the digital twin app Sentience far less useful for older generations. Journalism and history are fundamentally different disciplines: journalists capture the present moment, while historians piece together the past from whatever fragmentary records survived — a challenge that becomes vivid when trying to reconstruct what Stewart Alsop II actually said in a speech he gave in Soviet-era Moscow. The rationalist movement around figures like Eliezer Yudkowsky, which helped seed effective altruism and shapes thinking at places like Anthropic, may be strong on the technical mechanics of AI but weak on understanding how AI will actually play out in a human world — because humans are not, and have never been, purely rational actors.

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