Stewart Squared

Crazy Wisdom
Crazy Wisdom

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

  1. 1 g fa

    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 Google05:00 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 WhatsApp10:00 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 platforms15:00 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 decades20:00 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 complexity25:00 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 systematically30:00 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 competitors35:00 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 boards40:00 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 devices45:00 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 connectivity50:00 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 applications55:00 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...

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

  2. 9 lug

    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.

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

  3. 2 lug

    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 journalism5:00 Brent’s journalism background, friendship with major tech figures, and the idea of the three Steves in Steve Jobs’ story10:00 Why journalists usually stay objective, what the fourth estate means, and how the press acts as a check on power15:00 The press, patriotism, Cold War context, CIA tensions, and how journalists like Stuart and his uncle navigated American loyalty20:00 McCarthyism, fear, false accusations, and how brave reporting protected people and challenged demagoguery25:00 Russia as part West / East, Christianity, borders of identity, and the discussion shifting into Russian history30:00 Soviet-era travel, tech speeches, old-school publishing, and the problem of reconstructing the past without a digital trail35:00 History vs. journalism, archives, memory, and why preserving records matters for telling the story later40:00 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.

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

  4. 25 giu

    Episode #95: Schrodinger's Bubble: Nobody's Keeping Up, And That's Okay

    In this episode of the Stewart Squared podcast, host Stewart Alsop sits down with his father, guest Stewart Alsop II, to tackle corrections from last week's show before diving into the rapid pace of AI development and whether anyone can truly keep up. They explore Brian Chesky's new AI lab venture, Anthropic's controversial Fable release and subsequent restrictions by the US government, and Stewart's increasingly frustrating relationship with what he calls an "abusive superintelligence." The conversation shifts to immersive art experiences as Stewart Alsop II reports from Japan, comparing his visit to TeamLab's digital projection exhibits with his investment in Meow Wolf's physical installations. They discuss the business models behind immersive entertainment, the limits of current AI capabilities (spoiler: AGI definitely isn't here yet), and why FFMPEG might be the unsung hero of modern video software. The episode wraps with reflections on Japan's art island Naoshima and the future of live streaming the podcast. Timestamps 00:00 Welcome and experiment announcement: Stewart introduces a new fact-checking approach for the podcast, explaining how they'll correct previous episodes while maintaining their improvisational conversation style.05:00 Correcting last week's record: The hosts address three mistakes from the previous episode regarding Brian Chesky staying as Airbnb CEO, Anthropic's revenue numbers, and NVIDIA's history with Apple's Mac computers.10:00 The impossibility of catching up: Discussion of Stewart II's newsletter concept about falling behind in the AI race, examining Meta and XAI's struggles to compete with leading AI companies despite massive investments.15:00 Schrodinger's bubble theory: Stewart explores whether we're experiencing a tech bubble, comparing current AI acceleration to past technological shifts and discussing uncertainty around market valuations.20:00 Abusive superintelligence relationship: Stewart describes his frustrating experience with Anthropic's constant changes, quality degradations, and trust issues while building applications dependent on their AI models.25:00 Enterprise focus and philosophical concerns: Analysis of Anthropic's shift toward enterprise customers, their cult-like hiring practices, and concerns about effective altruism ideology influencing AI alignment decisions.30:00 Geographic restrictions and sovereignty: Discussion of Fable's sudden unavailability to non-US citizens, prompting exploration of Chinese AI models as alternatives for maintaining independence.35:00 Immersive entertainment comparison: Stewart II shares impressions from visiting TeamLab in Tokyo, comparing their digital projection-based experiences with Meow Wolf's physical installations and business models.40:00 TeamLab versus Meow Wolf analysis: Detailed comparison of how TeamLab uses programmable projections for repeatability while Meow Wolf builds physical environments, discussing advantages and challenges of each approach.45:00 Business model differences: Exploration of capital costs, repeat visitors, and sustainability challenges between TeamLab's digital flexibility and Meow Wolf's expensive physical build-outs in multiple cities.50:00 Live streaming ambitions: Stewart reveals plans to livestream future episodes using FFMPEG technology, discussing the technical challenges and open-source philosophy behind modern video streaming infrastructure.55:00 Japan's art island experience: Stewart II describes visiting Naoshima, an island dedicated entirely to art installations including works by David Hockney and Yayoi Kusama's famous pumpkin sculptures. Key Insights 1. The podcast experimented with a new format of correcting factual errors from previous episodes, including clarifications about Brian Chesky remaining as Airbnb CEO while building a separate AI lab, corrections to Anthropic revenue figures, and historical facts about NVIDIA providing GPUs to Apple products until around 2012-2013. This represents an effort to maintain journalistic accuracy despite the improvised nature of their conversations.2. A central thesis emerged around the impossibility of catching up in the AI race once a company falls behind. Examples include Meta's struggles despite aggressive researcher hiring and expensive talent acquisition, and XAI renting out unused data center capacity to competitors like Anthropic and Google for billions per quarter, suggesting their product is not achieving comparable usage to competitors despite massive infrastructure investment.3. The concept of Schrodinger's bubble was introduced to describe the current technological moment, where we exist in an uncertain state between revolutionary transformation and speculative excess. Unlike previous acceleration periods in the 1980s-2000s with personal computers or social media's emergence, this acceleration with AI appears unrelenting, and determining whether we are in a bubble is impossible until the bubble either continues or bursts, creating anxiety and excitement simultaneously.4. Anthropic faces criticism for degrading service quality and implementing paternalistic guardrails on their Fable model, including downgrading performance in certain domains like biotech and cybersecurity, sometimes without user notification. This approach to AI alignment, rooted in effective altruism philosophy, is viewed as potentially deluded and cult-like, prioritizing enterprise customers over individual users while destroying trust through policies like restricting non-US citizens from accessing certain features.5. The comparison between immersive entertainment experiences TeamLab in Japan and Meow Wolf reveals fundamentally different business models, with TeamLab using digital projection that can be easily reprogrammed versus Meow Wolf's expensive physical builds. TeamLab likely achieves more repeat business through constantly changing digital experiences, while Meow Wolf struggles with high capital costs and limited reasons for visitors to return, suggesting future convergence between these approaches.6. Current AI capabilities fall short of artificial general intelligence, as demonstrated by persistent failures to solve complex technical problems like real-time video lip syncing despite access to advanced models like Anthropic's Fable. While AI excels at deterministic software tasks with automated tests, it cannot handle subjective domains requiring taste like video production or immersive experiences, revealing fundamental limitations in current large language models.7. Open source technology like FFMPEG demonstrates how fundamental video and audio processing capabilities remain available to everyone on a level playing field, with major platforms like YouTube, Netflix, and Rumble all using the same underlying tools. This represents a successful counter-model to proprietary complexity from the 1990s, suggesting opportunities for new competitors to build sophisticated streaming and video capabilities without requiring the resources of established tech giants.

    Episode #95: Schrodinger's Bubble: Nobody's Keeping Up, And That's Okay

  5. 18 giu

    Episode #94: ARM Wrestling: NVIDIA's Quiet Coup Against Intel

    In this episode of the Stewart Squared podcast, host Stewart Alsop speaks with his father Stewart Alsop II, who joins from Tokyo while Stewart broadcasts from Buenos Aires at 5 AM his time. The conversation covers NVIDIA's new Spark chip announcement and its partnership with Microsoft to bring ARM-based processors to Windows PCs, finally allowing Windows to compete with Apple's performance gains from five years ago when they switched to their own ARM-based M-series and A-series chips. They discuss the competitive dynamics between chip manufacturers, the token apocalypse affecting AI coding assistants like Claude and Codex, and how companies like Anthropic are struggling with inference costs while renting data center capacity from SpaceX's underutilized X AI facilities. The discussion also touches on the rise of small models for on-device AI, the dominance of Chinese models in developing markets, SoftBank's ownership of ARM and history of big bets, and how attention and access to insider deals have shaped the AI investment landscape. For more context on Microsoft's strategy, Stewart Alsop II references a Ben Thompson Stratechery interview with Microsoft CEO Satya Nadella that helped clarify how Windows now runs on ARM architecture. Timestamps 00:00 Stewart Alsop welcomes listeners, explains recording at 5 AM his time, 5 PM in Tokyo Japan, discusses NVIDIA's new announcement about processors and chips for Windows computers05:00 Discussion of ARM architecture versus Intel chips, Apple's competitive advantage using ARM-based M-series processors, how Windows has fallen behind Macintosh in performance capabilities10:00 NVIDIA positioning new chip as AI-focused but actually ARM-based architecture, Microsoft modifying Windows to run on ARM, multiple manufacturers producing laptops with NVIDIA chips instead of Intel15:00 Deep dive into ARM licensing model, SoftBank ownership of ARM, how NVIDIA's CPU competes with Intel while Microsoft adapts Windows for ARM architecture20:00 Intel's competitive position, Microsoft's alliance with NVIDIA, discussion of GPU versus CPU functions, how graphics processing naturally supports training large language models25:00 Token apocalypse experience with Claude and Codex, rate limiting issues, moving between coding assistants, quality regressions and improvements in different AI coding tools30:00 Anthropic efficiency improvements with Opus 4.8, competitive dynamics between Claude Code and Codex, strategy of using multiple subscriptions to avoid rate limiting35:00 Chinese models as workhorses for global users who cannot afford expensive subscriptions, frontier models limited to Google Anthropic and OpenAI, affordability challenges internationally40:00 Small models running on devices versus cloud-based large models, Apple's WWDC expectations for integrating models on iPhone, personal computing productivity shifts45:00 SoftBank history with Masayoshi Son making big bets, ARM acquisition rationale, attention-based access to insider deals, comparison to celebrity entrepreneurs gaining investment access50:00 Historical perspective on insider access to deals and IPOs, closing remarks about continuing conversation from Japan Key Insights 1. Microsoft and NVIDIA announced a new ARM-based processor called Spark that will run Windows, marking a significant shift in the PC market. This represents Microsoft finally moving away from its dependence on Intel chips, similar to what Apple did five years ago when it introduced its M-series chips for Macintosh computers and A-series for iPhones. The development is positioned as an AI chip for marketing purposes, but the real significance lies in the ARM architecture, which NVIDIA has licensed. This alliance between Microsoft and NVIDIA directly challenges Intel's dominance in the PC processor market and could make Windows machines more competitive with Apple's Macintosh in terms of performance and efficiency.2. The competitive landscape in AI coding assistants has dramatically shifted, with Anthropic's Claude Code releasing version 4.8 that significantly improved code quality and token efficiency. After experiencing severe rate limiting issues in May due to inference capacity constraints, Anthropic made their coding model much more efficient at the token level, allowing users to accomplish more within existing subscription tiers. Meanwhile, OpenAI responded with Codex to compete with Claude Code's success from last December. This competition has created a situation where programmers are now splitting subscriptions between multiple services, paying for both Codex and Claude Code while using Chinese open-source models as fallback options when they hit rate limits.3. The token apocalypse revealed fundamental business challenges for AI companies as they struggle to balance inference capacity with growing demand. Anthropic had to make difficult decisions to prioritize enterprise customers over individual users, causing noticeable degradation in their chatbot product quality. The company was spending enormous amounts of inference capacity on making conversations feel natural and philosophically relevant, which proved financially unsustainable. Companies like Uber reportedly burned through their entire token budgets in just three months, highlighting how the rush to maximize token usage became a poor metric for actual productivity, falling victim to Goodhart's Law where a measure that becomes a target ceases to be a good measure.4. The revenue growth projections for Anthropic demonstrate the explosive commercial potential of large language models. The company expected to end 2025 with 9 billion dollars in revenue, but by the second quarter had revised expectations to 50 billion dollars. This astonishing growth comes from companies paying substantial enterprise budgets for AI services. Meanwhile, SpaceX's X AI data center, built rapidly but underutilized due to poor adoption, has been rented out to both Anthropic and Google for approximately 2 billion dollars per month collectively, showing how infrastructure built for one purpose can be repurposed when the original business model fails to generate sufficient demand.5. SoftBank's strategic bet on ARM five years ago positioned the company at the center of the current processor revolution. Founded by Masayoshi Son, SoftBank has a history of making large, bold investments over four decades, including early deals with Microsoft for software distribution in Japan. The company took ARM private and then public again, with SoftBank retaining majority ownership. This investment proved prescient as ARM's licensing model became increasingly valuable, especially as Apple, NVIDIA, and others adopted ARM architecture for their processors, making it the de facto standard for CPU design across multiple device categories from smartphones to personal computers.6. The future of AI appears to be splitting between small models running on devices and large frontier models in the cloud. Apple is expected to announce at WWDC its integration of Google models on the iPhone, utilizing small models that can run locally on the device for personal productivity tasks like calendar and email management, while connecting to cloud-based large language models for more complex operations like programming. This hybrid approach addresses both privacy concerns and cost efficiency, as running everything through cloud-based large language models proves financially unsustainable for everyday personal computing tasks. The industry consensus currently recognizes only three companies as leaders in frontier models: Anthropic, OpenAI, and Google.7. Chinese AI models are emerging as the workhorses for global markets due to a...

    Episode #94: ARM Wrestling: NVIDIA's Quiet Coup Against Intel

  6. 11 giu

    Episode #93: Too Big to Question: SpaceX, Wall Street, and the End of Accountability

    In this episode of the Stewart Squared podcast, host Stewart Alsop and guest Stewart Alsop II tackle the explosive SpaceX IPO, conflict of interest in politics and finance, and whether we're heading toward economic collapse or the singularity. The conversation kicks off with them acknowledging they had to restart recording after getting into a heated argument about whether Trump's stock trading and Nancy Pelosi's husband's trades fall into the same category of insider dealing—though neither technically qualifies as illegal insider trading. From there, they dig into the mechanics of the SpaceX IPO, questioning how Elon Musk convinced major banks like Goldman Sachs and Morgan Stanley to support a staggering $1.75 trillion valuation despite the company reporting nearly $5 billion in losses against $18.7 billion in revenue. Stewart II, who actually read the 300-page S-1 prospectus (unlike most people), explains how this IPO could fail and compares it to the infamous WeWork collapse. They explore the manual process still involved in IPOs, the role of stock exchanges from the Dow to NASDAQ to the new Texas Stock Exchange, and how Trump has concentrated executive power in ways that echo—and pervert—Teddy Roosevelt's use of executive orders. The discussion touches on reserve currencies, Argentina's economic history, cryptocurrency's death as a decentralized ideal, and whether the singularity is real or just conspiracy fantasy embraced by wealthy tech elites. Timestamps 00:00 Stewart Squared podcast begins with revealing an argument about Trump and Nancy Pelosi both doing insider trading though it's not technically illegal insider trading05:00 Discussion shifts to insider trading history from the Great Depression era and how current rules no longer work effectively with both politicians stretching ethical boundaries thin10:00 SpaceX IPO prospectus analysis begins with focus on Elon Musk's control and conflicts of interest as banks go along with questionable trillion dollar valuation for massive fees15:00 Investment banking history explored from boutique banks in seventies taking startups public to Internet bubble abuses and evolution through social media crypto and AI eras20:00 Stock exchanges worldwide discussed including NASDAQ origins in 1971, New York Stock Exchange history, and newer Texas stock exchange where Elon sells shares with fewer reporting rules25:00 Chevron principle explanation showing how Trump gathered executive power while claiming to fight deep state creating ironic situation of doing more executive overreach not less30:00 US dollar reserve currency status threatened by massive national debt and interest payments now consuming thirty percent of federal budget with neither party willing to balance accounts35:00 IPO mechanics and pricing discussed with SpaceX seeking up to two trillion valuation though market expects between one trillion and 1.6 trillion based on Polymarket betting40:00 Risk factors in SpaceX prospectus examined including losses of 4.9 billion against 18.7 billion revenue creating outrageous 300x price to sales ratio with Elon controlling 85 percent voting45:00 Argentina economic crisis comparison drawn from 1960s through 2001 Corralito when peso devalued from one-to-one with dollar to one-to-four overnight destroying savings50:00 Singularity discussion concludes episode calling it conspiracy fantasy while drawing parallels between Theodore Roosevelt's executive orders for public good versus Trump's for personal profit Key Insights 1. The discussion reveals a fundamental transformation in how stock markets and Initial Public Offerings function compared to historical norms. The SpaceX IPO represents an extreme example of this shift, with Elon Musk essentially controlling the entire process including valuation, pricing, and disclosure while investment banks like Goldman Sachs, Morgan Stanley, and JPMorgan simply comply because of the massive fees involved. The IPO aims to raise seventy-five billion dollars at a valuation approaching one point eight trillion dollars, despite the company reporting losses of four point nine billion dollars against eighteen point seven billion in revenue, creating a price-to-sales ratio around three hundred times, which defies traditional financial metrics that would normally support such a valuation.2. The conversation illuminates how conflicts of interest have become normalized at the highest levels of American finance and government. Trump is described as one of the most active stock market investors while serving as president, with correlations noted between his trades and policy announcements, yet this occurs in an environment where regulatory mechanisms no longer effectively constrain such behavior. The traditional checks and balances that prevented insider trading and conflicts of interest have been stretched so thin that nobody can agree on what constitutes inappropriate behavior anymore, creating a system where all rules have become negotiable for those with sufficient power and influence.3. The decline of traditional IPO processes reflects broader systemic changes in American capitalism. In the nineteen seventies and eighties, boutique investment banks would take startup companies public when they had thirty to fifty million in revenue at reasonable valuations, providing opportunities for companies to access public markets relatively quickly. That system was abused during the Internet bubble of the nineties, leading to companies going public and then declaring bankruptcy within months. Since then, the market has experienced successive bubbles in social media, crypto, and AI, with each cycle becoming progressively more detached from fundamental business metrics and increasingly difficult to distinguish sustainable businesses from speculative ventures.4. The role of stock exchanges has evolved significantly, with the NASDAQ emerging in 1971 specifically to serve technology companies while the New York Stock Exchange dates back to the 1890s. The conversation reveals that SpaceX is being included in the Dow Jones index immediately upon going public, rather than waiting the typical six months, and that Musk is also selling shares on the newly created Texas Stock Exchange where regulations are less stringent. This fragmentation of markets and willingness to bend traditional rules for high-profile offerings demonstrates how institutional guardrails have weakened, with exchanges competing for prestigious listings by offering more favorable terms rather than maintaining consistent standards.5. The discussion of reserve currency status reveals existential risks facing the American economy. The United States has maintained the dollar as the global reserve currency, which allows the country to borrow its way out of trouble because all other currencies are indexed to it. However, this system is being abused through massive national debt where interest payments now consume roughly thirty percent of the federal budget. Neither Republicans nor Democrats are willing to bring operating accounts back into balance, and there are now situations where countries trade currencies without reference to the dollar. If the United States loses reserve currency status, the country would face an Argentina-like scenario of economic collapse.6. The comparison between current conditions and historical economic crashes provides important context for understanding present risks. The speakers identify that the 2008 crash was triggered by real estate, the 2001 crash by the Internet bubble, and 2020 by the pandemic, but the trigger for the next crash cannot be predicted in advance. What makes the current situation particularly concerning is that multiple sectors appear overvalued simultaneously, with unsustainable practices across technology, finance, and government spending. The feeling expresse...

    Episode #93: Too Big to Question: SpaceX, Wall Street, and the End of Accountability

  7. 4 giu

    Episode #92: The $1.75 Trillion Bet: What WeWork Taught Us About the SpaceX IPO

    On this episode of the Stewart Squared podcast, host Stewart Alsop speaks with his father Stewart Alsop II about the SpaceX IPO and whether such a massive public offering could actually fail. Stewart Alsop II published his analysis just fifteen minutes before recording at sallsop.substack.com, questioning the logic behind the $1.75 trillion valuation and $75 billion raise, especially given that the company loses nearly $5 billion annually. The conversation ranges from the mechanics of IPOs and the SEC approval process to the only recent failed IPO (WeWork), SPACs versus traditional public offerings, the iron triangle of regulators and business interests, and comparisons between political figures' investment track records. Stewart Alsop II draws on his experience living through decades of Bay Area politics and business while analyzing whether institutions will actually buy into what he describes as a bet on Elon Musk rather than traditional fundamentals. Timestamps 00:00 Stewart introduces the episode topic returning to SpaceX IPO discussion and what he learned writing his article about IPO failures05:00 Discussion of how IPO conspiracy works between SEC regulators bankers and entrepreneurs creating iron triangle relationships that rarely result in failures10:00 WeWork becomes example of rare IPO failure when institutions refused to buy shares despite SEC approval and banker support15:00 Examination of Trump's public transparency about money-making versus traditional banana republic secrecy and oligarch networks20:00 Debate over World Liberty Financial investments and whether Trump family portfolio signals SpaceX IPO success potential25:00 Heated disagreement about Nancy Pelosi's husband's stock trading and conspiracy theories before ending the episode Key Insights 1. An IPO can fail after the S-1 filing is published, though it has only happened once in recent memory with WeWork. When Adam Neumann pushed bankers to file WeWork's S-1, institutional investors reviewed the disclosed information and refused to buy the stock, preventing the company from going public through the traditional IPO process. This demonstrates that while the SEC, bankers, and company founders may all approve of an offering, the ultimate gatekeepers are the institutional investors who actually purchase the shares.2. The SpaceX IPO represents an unusual situation where the company seeks a valuation of approximately 1.75 trillion dollars while only raising 75 billion dollars, representing roughly 2% of the company. This creates a challenging situation for potential investors because the upside is limited—for investors to make significant returns, SpaceX would need to become worth more than Apple, Google, or NVIDIA, all of which have twenty-year histories as public companies. This raises serious questions about the rational investment case for institutional buyers.3. Investment bankers have strong financial incentives to push IPOs through to completion, as they receive approximately 6% of the proceeds. In the SpaceX case, this would amount to 6% of 75 billion dollars. This creates a structural problem in the IPO process where bankers may not adequately filter out questionable offerings, relying instead on the SEC approval process and institutional investor appetite to serve as quality controls.4. Elon Musk has consolidated multiple companies into SpaceX before proposing to go public, including X AI and the company formerly known as Twitter, in addition to the core SpaceX rocket business and Starlink. While SpaceX itself generates about 4.5 billion in revenue and Starlink generates 11.5 billion in revenue growing at 50% annually, the company is currently losing almost 5 billion dollars per year. This makes the offering a bet on much more than just the space business, and Musk will control 85% of voting shares.5. SPACs, or Special Purpose Acquisition Companies, represent an alternative path to going public that bypasses the traditional IPO process. These shell companies go public at 10 dollars per share without having an actual operating business, then search for a private company to merge with. WeWork eventually went public through a SPAC after its traditional IPO failed, though the company later went bankrupt. Most SPACs, approximately 95%, trade below their original value, making them generally problematic investment vehicles.6. Elon Musk has successfully transformed two major industries through Tesla and SpaceX, which distinguishes him from other entrepreneurs like Adam Neumann who had not proven themselves before WeWork. Tesla proved the viability of electric cars, challenged dealer rules to sell directly to customers, built charging networks, and manufactured batteries at unprecedented scale. Similarly, SpaceX developed the reusable Falcon 9 rocket and built Starlink into a business three times larger than the rocket business itself, though Musk nearly went bankrupt three times in the process.7. The traditional IPO process involves an iron triangle between the SEC regulators, investment bankers, and company founders, with institutional investors serving as the final check on whether an offering succeeds. Approximately 98% of IPO shares are purchased by institutions rather than individual investors. The SEC requires companies to publish an S-1 disclosure document revealing all company details, and if this document passes SEC review, bankers then attempt to sell shares to institutional investors who make the ultimate decision about whether to participate.

    Episode #92: The $1.75 Trillion Bet: What WeWork Taught Us About the SpaceX IPO

  8. 28 mag

    Episode #91: The $1.5 Trillion Question: Why SpaceX's IPO Math Doesn't Add Up

    In this episode of the Stewart Squared podcast, host Stewart Alsop and his father Stewart Alsop II dig into the major AI and tech IPOs hitting the market, with SpaceX leading the charge at a controversial $1.5 trillion valuation despite just $20 billion in revenue. They break down how SpaceX's massive S-1 filing (so big it crashed Claude's context window) reveals a company now bundling together Starlink, rocket launches, X (Twitter), and the struggling xAI/Grok business—with key researchers having already jumped ship after getting their SpaceX stock. The conversation covers Anthropic's explosive revenue growth (projecting $10 billion in Q2 alone) and their smart move renting Musk's underutilized data center for $1.25 billion, OpenAI's pending IPO, Apple's quiet but strategic AI approach using on-device models and partnering with Gemini instead of OpenAI, and why the institutional investors might balk at SpaceX's aggressive pricing when the IPO drops on June 12th. Stewart II shares his contrarian take: he'd never touch SpaceX stock at this valuation but is seriously considering Anthropic, while explaining the arcane details of revenue recognition, vesting schedules, and why Elon Musk's singular track record lets him operate by different rules than any other CEO. Timestamps 00:00 Welcome and SpaceX IPO discussion begins, exploring the $20 billion valuation and mathematical implications of the massive offering05:00 Anthropic renting Musk's data center for over a billion monthly while Grok struggles, researchers leaving xAI after receiving SpaceX stock10:00 Institutional investors may decline SpaceX shares at ridiculous valuation compared to Apple's $400 billion revenue and NVIDIA's profitability15:00 Apple's on-device AI strategy with small models and Gemini integration while Musk fails in foundation models20:00 Revenue recognition differences between companies, Anthropic projecting $10 billion quarterly revenue with conservative accounting practices25:00 SpaceX revenue breakdown showing Starlink at $11 billion dominating over rocket business, Twitter and xAI tucked into valuation30:00 Comparing SpaceX's $20 billion revenue to Apple's $400 billion while discussing material disclosure requirements in IPO filings35:00 Musk's singular achievement changing space and car industries, earning unprecedented valuation despite rational market concerns40:00 Argentine politics and Milei's challenges, parallels to Trump's midterm influence and Peter Thiel's strategic positioning45:00 Final thoughts on IPO opportunities, avoiding SpaceX at current valuation while considering Anthropic's rapid growth potential Key Insights1. SpaceX is going public at a 1.5 trillion dollar valuation while generating only 20 billion in revenue, creating significant concerns about whether the IPO will succeed. The company is attempting an unusually fast timeline from S-1 filing on May 20th to going public on June 12th, bypassing the typical two month roadshow process. There is a real possibility the offering could fail because institutional investors who must buy 70% of the shares may decline at this valuation, seeing no path for the stock to appreciate further.2. The valuation appears disconnected from fundamentals when compared to companies like Apple with 400 billion in revenue worth 4 trillion or NVIDIA with 85 billion in revenue worth 5 trillion. SpaceX would need to grow revenue from 20 billion to potentially 100 billion and achieve profitability to justify even being in the multi-trillion dollar valuation range. The aggressive pricing likely comes from Musk himself rather than the investment banks, as he controls the process with his ownership structure.3. Anthropic is experiencing explosive revenue growth, jumping from 4 billion in one quarter to a projected 10 billion in the second quarter, putting them on track for 40 to 50 billion in annualized revenue. Most remarkably, they claim they will be profitable in the second quarter, which would be unprecedented for a foundation model company. Their strategic deal to rent Musk's underutilized data center for 1.25 billion monthly solved their infrastructure problems while giving Musk revenue to cover his failed Grok investment.4. Elon Musk consolidated multiple companies including Twitter, xAI, SpaceX and Starlink into one entity still called SpaceX, creating a complex conglomerate that will be difficult for investors to evaluate. The xAI portion has essentially failed as a foundation model competitor, with dozens of researchers leaving after the merger gave them valuable SpaceX stock as an exit. Twitter contributes roughly 2 billion in revenue, the rocket business does 4 billion, but Starlink is the real driver at 11 billion and growing rapidly.5. Apple has been quietly working on small on-device AI models embedded in their operating systems rather than pursuing foundation models, and they will likely deliver on their 2024 promises using Google's Gemini instead of OpenAI. This strategic approach of focusing on practical on-device capabilities while partnering for cloud capabilities may prove more successful than trying to build their own foundation model. The company avoided the mistake of announcing capabilities before they were ready, then pragmatically adjusted their approach.6. Once you fall behind in the foundation model race, you cannot catch up, which explains why both Musk with Grok and Zuckerberg with Meta have struggled despite massive investments. The leaders like Anthropic and OpenAI have such strong momentum and embedded positions that competitors cannot overcome the gap. This dynamic is similar to how Palantir embedded itself so deeply in government and commercial customers before LLMs that they remain entrenched despite new AI capabilities.7. The simultaneous IPOs of SpaceX, OpenAI and Anthropic represent different investment propositions, with SpaceX being personality and potential driven, OpenAI having revenue recognition questions, and Anthropic showing the strongest fundamentals with explosive growth and a path to profitability. All three will have founder-controlled voting structures similar to Meta where Zuckerberg has 60% control, allowing these leaders to pursue long-term visions regardless of public market pressures. The timing is largely coincidental rather than coordinated, driven by each company's specific capital needs and market conditions.

    Episode #91: The $1.5 Trillion Question: Why SpaceX's IPO Math Doesn't Add Up
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Descrizione

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

Informazioni

  • Canale
    Crazy Wisdom
  • Creatore
    Stewart Alsop II, Stewart Alsop III
  • Anni di attività
    2024 - 2026
  • Puntate
    98
  • Classificazione
    Contenuti espliciti
  • Copyright
    © @ Galactosa
  • Sito web del podcast
    Stewart Squared

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