1 day ago

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.

59 min

18 Jun

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...

52 min

11 Jun

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...

53 min

4 Jun

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.

25 min

28 May

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.

46 min

21 May

Episode #90: Nobody Knows What Software Is Worth Anymore

In this episode of the Stewart Squared podcast, host Stewart Alsop II connects from Tangier, Morocco while his son Stewart Alsop III digs deep into the technical challenges of building video conferencing software, specifically tackling the notorious lip sync problem that's consumed his last two months. The conversation moves from mutation testing and DevOps to exploring the future of software consulting, examining why Silicon Valley has long held a visceral distrust of consultants while contractors thrive, and what AI-powered development means for how software gets built and sold in the coming years. Stewart III shares his journey from "vibe coding" to implementing scientific methods in his development process, while his father draws on decades of experience as both a journalist and investor to contextualize the shifting landscape of enterprise software, touching on everything from the rise of SaaS to why companies like Riverside raised $80 million while Stewart III builds competing technology solo in his head. Timestamps 00:00 Welcome from Morocco, Stewart Senior joins from Tangier with Middle Eastern backdrop, Stewart Junior deep in AI development learning mutation testing, integration tests, unit tests, red to green testing05:00 Discussion of vibe coding evolution to scientific method coding, working on lip sync white whale problem for two months, building pipeline from recording to post-production using FFMPEG diagnostics10:00 Explanation of how recording works with separate audio and video streams, discovery that browser clocks using tiny crystals don't keep accurate time, learning about MediaRecorder API versus WebCodecs advantages15:00 Debate about competing with Riverside's 80 million dollar funding, discussion of building specialized software versus SaaS products, exploring turnkey podcasting solutions and business models20:00 Deep dive into consultancy business model, Stewart Senior's visceral hatred of consultants, discussion of business school graduates becoming consultants or bankers, Microsoft's deliberately small consulting practice25:00 Exploration of conflict of interest in journalism and investing, disclosure requirements, comparison to New York Times OpenAI lawsuit, discussion of father's unpaid consulting role in DC power centers30:00 History of consultancies like Arthur Andersen and PricewaterhouseCoopers, role in mergers and acquisitions, example of David Ellison buying Paramount and pursuing Warner Brothers Discovery35:00 Difference between contractors and consultants, discussion of outsourcing to India, Cloud Factory in Nepal, Ronald Coase economics, Infosys as first big software engineering consultancy40:00 Stewart Junior's ability to understand code concepts without reading code, using scientific method and chaos monkey development, Netflix streaming techniques, debugging through sufficient motivation45:00 Sales challenges and negotiation skills in family, working with mentor Zavant on sales frameworks, generosity versus transactional relationships, Turkish bazaar negotiation culture comparison50:00 Discussion of value creation and belief in sellability, the 80/20 rule of product completion, Adam Neumann and Travis Kalanick examples, Elon Musk as builder not salesman creating entire systems Key Insights 1. The challenge of solving technical problems reveals the importance of understanding methodologies over mastering code itself. Stewart Alsop III spent two months wrestling with a lip sync problem in his video recording system, learning about mutation testing, integration tests, and DevOps along the way. The key insight is that he does not need to read or write code directly anymore. Instead, he needs only a conceptual understanding of frameworks like the scientific method or chaos engineering to direct AI systems to solve complex technical problems. This represents a fundamental shift where domain knowledge and problem articulation matter more than programming expertise.2. Modern video conferencing systems create synchronization challenges because different computers use tiny crystals to keep time, but these crystals do not maintain perfect accuracy, especially when network conditions fluctuate. The problem is not simply about recording separate audio and video streams and reassembling them. Instead, systems create containers with audio and video together while also recording separate audio tracks, and all these different clocks drift apart from each other. This explains why lip sync issues plague even well funded platforms like Riverside, and why solving this problem requires sophisticated diagnostic systems and conversion pipelines using tools like FFMPEG.3. The evolution of software business models reflects changing technological constraints and market conditions. In the 1990s and early 2000s, software was sold as one time purchases, often on physical media like cartridges or floppy disks. The shift to Software as a Service in the 2010s happened because it was considered better for customers who did not have to pay large upfront fees and because cloud infrastructure made it feasible. Now, with AI enabling individuals to build complex software themselves, we may be entering another transition period where the SaaS model itself becomes obsolete, though what will replace it remains unclear.4. Programming represents the first domain where artificial general intelligence has effectively arrived because programming consists entirely of text. Unlike domains involving physical manipulation or subjective judgment, code can be completely represented in language, and decades of open source code provide massive training datasets. This explains why tools like Claude have become so powerful so quickly in programming contexts, and why Anthropic claims that most of its models are now generated by AI systems themselves. The recursive nature of AI writing code to improve AI represents a fundamental breakthrough that does not yet exist in other domains.5. Consultancies emerged to solve problems that companies could not efficiently solve themselves, but their value proposition is eroding. Large consulting firms like the Big Seven accounting firms grew powerful by integrating complex enterprise software and managing mergers and acquisitions. However, as software becomes easier to build and modify through AI, and as the difficulty of integration decreases, the justification for expensive consultancies diminishes. The antipathy toward consultants in Silicon Valley stems from a belief that they represent companies paying others to think for them rather than developing internal capabilities, and this critique becomes more valid as technical barriers fall.6. The distinction between contractors and consultants matters for understanding business models and value creation. Contractors are individuals or small teams hired for specific projects who sell their labor directly. Consultancies are businesses built around winning large contracts and then deploying teams to execute them, often with substantial markup. The emergence of platforms like Upwork and the phenomenon of outsourcing to places like India, Nepal, and Kenya created hybrid models where individual profiles often mask small consultant operations. Understanding these distinctions helps clarify what kind of business model makes sense for someone developing new technical capabilities.7. Believing in the value of what you create is a prerequisite for being able to sell it, and products must be truly finished before they have sellable value. The last twenty percent of any project, whether writing, programming, or product development, represents the hardest work because it involves transforming something functional into something polished and complete. Until the lip sync problem is definitively solved, the video recording system remains a prototype rather ...

52 min

14 May

Episode #89: Vibe Engineer Meets Venture Capitalist: A Father-Son Dispute About the Future

In this episode of Stewart Squared, host Stewart Alsop sits down with his father, Stewart Alsop II, for a wide-ranging conversation that moves from the technical to the historical to the financial. The two kick things off with Stewart's self-proclaimed evolution from "vibe coder" to "vibe engineer," as he tackles the tricky challenge of audio and visual sync in his own custom podcast recording software, positioning it as a direct competitor to platforms like Riverside.fm and Squadcast. From there, they get into a business breakdown of OpenAI and Anthropic, debating whether Claude's recent stumbles are a blip or a sign of deeper trouble, and what an IPO would actually mean for both companies as they look to compete with the big players. The conversation winds through a rich history of personal computing — from Mosaic and Netscape to PageMaker and the LaserWriter, desktop publishing, the browser wars, and how Windows 95 and the early internet reshaped everything — before landing on the turbulent state of the airline industry, the fallout from the Strait of Hormuz blockade, and what the collapse of Spirit Airlines says about fragile business models. Timestamps 00:00 - Stewart introduces vibe engineering, tackling audio-visual sync problems while others debate AI coding tools.05:00 - Deterministic vs probabilistic software discussed, with Stewart building real engineering skills through coding challenges.10:00 - Browser history explored, from Mosaic origins at University of Illinois to Netscape's proprietary commercialization.15:00 - Adobe Flash wars with Steve Jobs examined, leading into desktop publishing revolution with PageMaker and LaserWriter.20:00 - PostScript origins at Xerox PARC discussed, Adobe founders transforming page composition from compositors to editors.25:00 - Kinkos, Windows vaporware, and personal computing evolution from 1985 through Windows 95 emergence.30:00 - Information Superhighway era examined, Netscape on Windows 95 driving personal computer mainstream adoption.35:00 - Claude versus Codex battle analyzed, Anthropic's trust erosion among engineers and Silicon Valley insider bubble.40:00 - OpenAI versus Anthropic growth metrics compared, IPO strategies and public market ambitions dissected.45:00 - Stock fundamentals explained through Tesla versus traditional automakers, quarterly earnings disclosure requirements.50:00 - Airline complexity breakdown, Spirit Airlines collapse tied to jet fuel hedging failures post-Iran blockade.55:00 - New capitalism emerging through AI, IPO mechanics enabling OpenAI and Anthropic to compete with tech giants.01:00:00 - Meta, Apple, Microsoft AI strategies compared, Chinese model competition driving Anthropic's existential decisions.01:05:00 - Surveillance states, sovereign nations, and India versus small countries as future nonaligned powers debated. Key Insights 1. There is a meaningful distinction emerging between types of AI-assisted builders. Actual engineers use AI tools to boost productivity while still understanding code. Vibe coders use prompt engineering to build things without formal training. And then there are people who have no interest in building software at all because they simply do not need to.2. Deterministic software is fundamentally different from probabilistic AI outputs. While the current hype around AI agents and markdown-based workflows is real, the underlying products are often insecure and unreliable. Building deterministic software first and layering in AI agents later is a more stable and trustworthy approach.3. Desktop publishing in the mid-1980s was a landmark moment in personal computing. The combination of the Apple Macintosh, PageMaker, and the LaserWriter printer transferred control of page composition from professional compositors to individual editors and writers, democratizing the ability to produce print materials.4. The browser wars of the 1990s, particularly Netscape running on Windows 95, marked the moment when the personal computer became meaningful to ordinary people. Before that, roughly a decade passed where developers and companies were still figuring out how operating systems, platforms, and application development were supposed to work together.5. The MediaRecorder API is a significant but underappreciated limitation in modern browser development. Because Safari does not support it in the same standardized way as Chrome and Chromium-based browsers, many podcast and recording platforms are effectively locked to Chrome, creating an opening for alternative technical approaches.6. Going public through an IPO gives companies like OpenAI and Anthropic access to capital at a scale that private fundraising cannot easily match. It also imposes mandatory quarterly financial disclosures, which means the public will finally be able to see actual revenue, spending, and growth figures rather than relying on perception and valuation claims.7. Airlines represent one of the most operationally complex businesses in existence, involving gate leases, dynamic ticket pricing, fuel costs, crew logistics, and massive debt structures. The sudden spike in jet fuel prices following the US blockade of the Strait of Hormuz exposed airlines that had not hedged their fuel costs, contributing directly to Spirit Airlines going out of business.

1hr 7min

7 May

Episode #88: Conspiracy Factist vs. Practical Capitalist: The Alsop Debate

In this episode of the Stewart Squared podcast, host Stewart Alsop III and his father Stewart Alsop II tackle the state of Silicon Valley, questioning whether it's been captured by corporate interests and discussing how they can maintain an independent voice in technology commentary. Stewart presents a manifesto for building the show in public while avoiding the pitfalls of podcasts like All In and the Technology Brothers Podcast Network (which was recently acquired by OpenAI). The conversation explores the friction between Stewart's millennial conspiracy-factist perspective and Stewart II's boomer practical capitalist viewpoint, covering everything from journalistic integrity and the Extropians movement to AI companies like Anthropic and OpenAI. They debate whether Silicon Valley operates as a conspiracy or simply reflects individual actors pursuing their own interests, discuss the degradation of Claude's performance and shrinkflation in AI services, and examine Apple's secretive corporate culture. Stewart III announces his move toward open source Chinese models and building his own "digital castle" independent of captured institutions, while Stewart II reflects on his fifty years observing the tech industry and maintaining an observer's stance that identifies with consumers rather than companies. Show notes mentioned:- Episode with Jim Ward about TK Media (his father's fund)- Crazy Wisdom interview with SpaceTime DB about real-time data infrastructure Timestamps 00:00 Stewart introduces new podcast format focused on building in public and explains TK Media fund background05:00 Discussion of Silicon Valley's capture and corruption, comparing independent voices versus bought podcasts like All In and Technology Brothers10:00 Stewart argues for maintaining journalistic integrity and restraint that differentiates them from paid influencers in tech15:00 Debate on conspiracy versus corruption in Silicon Valley, with generational perspectives on technology industry evolution20:00 Stewart's father shares concerns about inability to agree on national purpose and economic anxieties about wealth preservation25:00 Deep dive into Extropians movement and its influence on modern AI research culture through Less Wrong community30:00 Analysis of Anthropic versus OpenAI business models and public benefit corporation status discussion35:00 Security trust levels across tech companies including Amazon, Apple, Microsoft and Google infrastructure comparison40:00 Product strategy challenges in AI space and Elon Musk's conditional Cursor acquisition deal analysis45:00 Stewart's migration strategy from Claude to open source Chinese models due to quality degradation and cost sensitivity50:00 Small models discussion preview and Apple Intelligence approach, planning future episodes on real time technology Key Insights 1. The podcast is establishing itself as an independent voice in technology media at a time when many major tech podcasts have been captured by corporate interests. The hosts point out that Technology Brothers Podcast Network was recently purchased by OpenAI and reports to their political operative, while other prominent shows like All In and Acquired have become platforms where hosts primarily talk their book. This creates a landscape where genuinely independent critical analysis of the technology industry has become rare, making the show's commitment to journalistic integrity and restraint particularly valuable for listeners seeking unbiased perspectives.2. The generational friction between the hosts creates a unique analytical framework for understanding Silicon Valley. The boomer perspective brings decades of experience observing the evolution of transformative technology since the PC era and the internet, while the millennial viewpoint offers contemporary insights into current technological developments and their social implications. This dynamic produces what they call creative tension, where disagreements about conspiracy theories versus practical capitalism lead to deeper explorations of industry trends. The absence of Generation X and Generation Z voices is noted but the existing dynamic provides sufficient diversity of thought to challenge assumptions and avoid echo chamber effects.3. Anthropic has distinguished itself from OpenAI through disciplined business practices and consistent strategic execution. As a public benefit corporation, Anthropic must report on public benefit alongside financial results, which creates accountability beyond pure profit motive. The company demonstrated this commitment by withholding the release of their Mythos model initially to allow organizations time to fortify their security, a decision some interpreted as conspiratorial but which actually reflected responsible AI safety practices. In secondary markets, Anthropic shares are valued higher than OpenAI despite smaller funding rounds, suggesting investor confidence in their path to profitability and their methodical approach to expanding functionality for enterprise customers.4. The AI industry is experiencing significant product management challenges and rapid shifts in business models. Claude made what the hosts describe as a legendary fumble in early March when service quality degraded significantly while the company initially denied problems, leading many users to lose trust and consider switching to open source alternatives. OpenAI responded to competitive pressure from Anthropic by introducing Codex, and the industry is moving away from unlimited usage models toward consumption-based pricing. This transition is forcing users to make economic decisions about which platforms to use, with corporate customers and well-funded startups likely staying with premium services while individual developers and smaller operations migrate toward open source Chinese models.5. Apple continues to operate with extraordinary secrecy that could be characterized as conspiratorial, though this reflects consistent strategic discipline rather than malicious intent. The vast majority of Apple's employees, estimated at around one hundred sixty-six thousand with most in retail, have never accessed the Apple campus where core product development occurs. The recent leadership transition where Tim Cook becomes executive chairman while focusing on global relationships, particularly with China, suggests Apple is managing complex geopolitical arrangements that require high-level diplomatic engagement. The company's market share in China has increased dramatically recently, indicating these strategies are producing results despite the opaque nature of the arrangements.6. The hosts identify a fundamental crisis in trust and shared purpose across American society that extends beyond technology into economic and governmental institutions. There is widespread inability to agree on basic facts or institutional reliability, creating anxiety about financial security and the stability of stored wealth. This represents not a coordinated conspiracy but rather an accumulation of incremental changes since World War Two that have led to confusion about governmental responsibility and social organization. The challenge of operating in this environment requires developing frameworks for evaluating which institutions deserve trust, with infrastructure providers like Amazon and Apple generally demonstrating better security practices than companies like Microsoft whose architecture requires security to be applied rather than built in fundamentally.7. The future of AI development will likely center on small on-device models rather than exclusively cloud-based large language models. Appl...

55 min

Stewart Squared

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

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

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

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

Episode #90: Nobody Knows What Software Is Worth Anymore

Episode #89: Vibe Engineer Meets Venture Capitalist: A Father-Son Dispute About the Future

Episode #88: Conspiracy Factist vs. Practical Capitalist: The Alsop Debate

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