Stewart Squared

In this episode of the Stewart Squared podcast, host Stewart Alsop sits down with his father Stewart Alsop II to unpack how private equity, venture capital, and growth equity have evolved—and possibly converged—since the 2008 financial crisis. They explore how massive liquidity injections in 2020 fueled a PE buying spree, why the distinctions between investment categories are blurring as everyone gets measured by the same metrics (DPI—distributions per investment), and whether the whole system is starting to break down. The conversation touches on everything from SPACs making a comeback to Elon Musk's SpaceX IPO, the financialization of restaurants, and how even the meaning of terms like "bank" and "cash" are shifting in real time. In the final segment, they bring on surprise guest Tommy Yu, CEO and founder of TurnOn Technologies, to discuss closed-loop payment systems, stored value, and why traditional finance people struggle to see beyond Visa and Mastercard logos even when companies like Starbucks are sitting on $2 billion in unredeemed gift card float.

Timestamps

00:00 Introduction and experimental format with a surprise guest, discussing private equity shifts from 2008 through 2023 and massive liquidity changes fueling PE buyouts
05:00 Historical perspective on venture capital evolution starting from the seventies, pension fund rule changes allowing risky investments, and the emergence of hedge funds and private equity differentiation
10:00 Growth equity versus private equity distinctions, SPACs making a comeback after previous failures, and the shrinking number of public companies from 7000 to 4000
15:00 The fundamentals of capitalism and time value of money, government money printing increasing liquidity, and how pension funds now invest heavily in alternative assets
20:00 DPI measurement becoming universal across all investment types, distinguishing between realized and unrealized gains, and how capital calling works in venture funds
25:00 Limited partnerships structure in venture capital, restaurant investments as different from tech startups, and Main Street versus Silicon Valley business models
30:00 Pension funds controlling massive percentages of national assets, asset allocation strategies across different investment vehicles, and everything being measured the same way now
35:00 How institutional definitions are breaking down, terms losing their original meanings, banks becoming something entirely different, and companies essentially functioning as banks themselves
40:00 Chinese centralized system appearing more effective than Western capitalism currently, enlightened dictatorship efficiency, and how professional attention demands are increasing dramatically
45:00 Vibe coding distinctions and keeping up with changing terminology, inviting guest Tommy Yu to discuss his fintech company TurnOn Technologies and stored value systems
50:00 Liberty Mutual's restaurant investment treating food as tradable assets, closed loop versus open loop systems like Starbucks' 8 billion dollar gift card float earning interest
55:00 Regulation falling behind fintech innovation, crypto remaining largely unregulated, and how intelligent investors can differentiate when everyone's doing the same thing
60:00 TurnOn Technologies creating universal stored value systems beyond single companies, neobanks as digital tellers, and younger generations caring more about user experience than traditional banking

Key Insights

1. The venture capital and private equity landscape has fundamentally transformed since the seventies when pension funds were first allowed to invest in risky alternative assets. What began as distinct categories with different purposes and metrics has now blurred together, with all investment types being measured by the same standard called DPI or distributions per investment dollar. This measures how quickly investors get their capital back in cash, and the problem is that venture capital now takes fifteen plus years to return capital compared to the historical five to ten years, making it less attractive when banks offer five percent and public markets offer ten percent returns.
2. Private equity has exploded to encompass over thirty six thousand companies in the United States, far exceeding the roughly four thousand public companies that exist today, down from seven thousand in nineteen ninety six. This represents a massive shift where the ownership layer has moved increasingly into private hands, with companies staying private longer and accessing capital through growth equity and private equity rather than going public. The lines between venture capital, growth equity, and private equity have become so blurred that even experienced investors struggle to articulate meaningful differences between these categories.
3. The financial system is becoming increasingly opaque and unregulated despite the perception that finance is highly regulated. New categories like private credit have emerged as what some consider cesspools of activity that regulators cannot keep pace with or even understand. The regulatory framework has fallen so far behind the actual innovations in fintech, crypto, and alternative investments that there is effectively no meaningful oversight in many areas, creating opportunities for both innovation and potential abuse that would have been impossible in previous eras.
4. The fundamental terms and definitions that underpin capitalism are changing so rapidly that experienced investors and business people can no longer rely on historical understanding. What constitutes a bank, what money actually means, what liquidity represents, and even what a company is have all shifted dramatically. Cash itself has become metaphorical rather than physical, and businesses are increasingly functioning as their own banks by holding stored value and earning interest on customer deposits, as demonstrated by Starbucks running eight billion dollars through gift cards with two billion in unredeemed float.
5. The Chinese communist system under Xi Jinping is currently working better for its people than the capitalist system is working for citizens of capitalist countries, creating an existential challenge to the assumption that capitalism is the superior economic model. This represents a historic shift where an enlightened dictatorship with strategic central planning is outperforming the chaotic and unpredictable leadership in capitalist democracies. The comparison suggests that the ideological certainty about capitalism's superiority may need to be reconsidered in light of actual results for ordinary citizens.
6. The democratization of investing through new structures like SPACs and the accessibility of markets has created a situation where the distinction between legitimate investment vehicles and pyramid schemes has become uncomfortably narrow. The entire system increasingly relies on later investors buying out earlier investors at higher valuations, with the public markets serving as the final exit for private investors to realize gains. This raises uncomfortable questions about whether the fundamental structure of modern capitalism differs meaningfully from the Bernie Madoff scheme that collapsed fifteen years ago.
7. The restaurant industry and Main Street businesses represent a parallel economy that operates on completely different principles than Silicon Valley startups, yet investment vehicles are increasingly trying to treat them as comparable assets. Liberty Mutual investing three hundred twenty million dollars in restaurant food credits represents the financialization of everything, where even meals become tradable assets divorced from the underlying business reality. This demonstrates how the investment world is desperately seeking returns in increasingly exotic and risky categories as traditional distinctions break down an...

In this episode of Stewart Squared, Stewart Alsop III and Stewart Alsop II dig into the fast-moving world of self-driving cars — from Cruise's rocky history and Waymo's expansion to Zoox's driverless design and NVIDIA's growing role as the go-to OEM partner for automakers building software- and AI-defined vehicles — before the conversation branches into dual-use tech lessons from Ukraine, the US-China race in autonomy and semiconductors, the DRAM memory shortage squeezing Apple and the gaming industry, Slate's stripped-down electric truck, and a closing riff on performance-based, subscription-driven podcasting.

Timestamps

00:00 — Self-driving cars and the fallout from Cruise’s collapse; where AVs are operating now, plus the role of NVIDIA in autonomous vehicles.
00:05 — Software-defined cars, Level 4 autonomy, and why Waymo and Zoox matter; how regulation and city-by-city permits shape rollout.
00:10 — Dual-use autonomy in war zones, liability, and the shift from consumer adoption to fleet adoption; NVIDIA’s place as a supplier, not a prime.
00:15 — Why NVIDIA is strategically positioned across chips, software, and cars; comparisons with Tesla, Rivian, Ford, and GM.
00:20 — China’s open-source AI push, RISC-V, control vs openness, and the tension between state control and innovation.
00:25 — The rise of neo-primes like Anduril, how the Pentagon buys systems, and why the U.

S. defense market favors trust and scale.
00:30 — A broader debate on capitalism vs communism, China’s economic strain, and whether the future looks more centralized or decentralized.
00:35 — The memory crisis: DRAM, GPUs, Apple, and video games getting squeezed as AI demand drives prices up.
00:40 — Cheap EV disruption with Slate, new form factors, and how car design may split between utility-first and premium autonomous vehicles.
00:45 — The city itself changing: AVs, urban planning, flying-car ideas, and how autonomy could reshape Los Angeles and beyond.
00:50 — Pricing scarce compute, NVIDIA’s leverage, and the idea that some companies become scarce to themselves.
00:55 — A long-range view: centralized vs decentralized tech cycles, RISC-V, and the possibility of open hardware reshaping everything.

Key Insights

1. Autonomous vehicles are consolidating around a small set of platform players. Waymo, Tesla, Zoox, and Rivian lead the US market, while China's fleet is dominated by companies like Geely-backed operations. Regulation moves city by city, so scale depends as much on winning local approval as on the technology itself.
2. NVIDIA has built a stealth position as the OEM backbone of autonomous vehicles. Rather than compete with carmakers, NVIDIA supplies the compute platform nearly every manufacturer relies on, letting it profit from the industry's growth without taking on liability or betting on any single winner.
3. Liability, not technology, is the biggest brake on driverless fleets. Once a car has no human driver, responsibility shifts entirely to the fleet owner. Zoox's steering-wheel-free design in San Francisco is the clearest test case for how that liability question gets resolved.
4. The DRAM shortage is a downstream effect of the AI buildout. Massive GPU demand for LLM training soaked up memory supply, driving up DRAM prices for everyone else — squeezing Apple, gaming consoles, and now the auto industry, which needs growing amounts of compute per vehicle.
5. China's dominance in EVs coexists with deep structural weakness. A shrinking, aging population, price wars that erase margins in sectors like solar, and a government that reins in entrepreneurs when they get too powerful (DeepSeek's blocked IPO) complicate the narrative of unstoppable Chinese industrial growth.
6. "Scarce to itself" describes a new kind of market power. Companies like NVIDIA and Apple aren't scarce because of demand tricks — they're supply-constrained on their own hardware, which lets them set pricing on their own terms rather than compete on it.
7. Podcasting may be shifting from ads to direct subscription models. Inspired by ideas like Trump's paid early-access posts, Stewart Alsop and Stewart Alsop II are testing "performance podcasting" — charging listeners directly for real-time access instead of relying on sponsorships.

In this episode of Stewart Squared, Stewart Alsop III and Stewart Alsop II sit into the shifting ground beneath the AI boom, starting with the strange saga of Leopold Aschenbrenner's hedge fund and the memory chip shortage that's rippling through everything from Apple's product line to the gaming industry, before moving into how OpenAI and Sam Altman's data center spending is reshaping global compute demand, the widening gap between American and international tech ecosystems, China's uneasy relationship with open source AI, and a look at Mira Murati's new venture Thinking Machines Lab and its fine-tuning tool Tinker, wrapping up with some thoughts on what a live, interactive version of the show could look like.

Timestamps

00:00 AI bubble and the Leopold Aschenbrunner hedge-fund story; debt, margin pressure, and why memory stocks surged
00:05 memory becomes the bottleneck as AI data centers expand; Apple, Micron, and rising RAM prices
00:10 Korea takes a hit from memory-market swings; contrast with Japan, manufacturing, and the karetsu model
00:15 Live translation tech, Google’s new API, and a side discussion of robotics and Japanese PC history
00:20 Japan’s early PC ecosystem, NTT, Microsoft, Windows, and why standards won the market
00:25 Back to finance: equity vs debt, leverage, Glass-Steagall, and how banking got reorganized
00:30 AI hedge funds, risk, Citadel, margin calls, and the distinction between lending and investing
00:35 Capitalism by starting companies vs buying companies; why money is partly a metaphor
00:40 AI agents, Chrome, and the difference between distributed and federated systems
00:45 Matrix and Nostr, open-source messaging, and the internet’s new borders
00:50 Live audience questions, interactive publishing, and the business potential of real-time conversation

Key Insights

1. The AI boom is straining a memory chip supply that can't scale fast enough. Massive spending on AI data centers by companies like OpenAI has driven demand for DRAM through the roof, and because memory factories take years to build, prices have roughly tripled in six months — squeezing everyone from Apple (which is struggling to ship products like the Mac mini) to the broader computer gaming industry.
2. Leverage is what turned a smart bet into a crisis. Leopold Aschenbrenner's "Situational Awareness" hedge fund quadrupled investor money early on by going heavy into AI, but borrowing tens of billions against volatile chip and memory stocks left him exposed when prices tanked — prime brokers like Bank of America, Goldman Sachs, and JPMorgan Chase issued margin calls, and Citadel ultimately bought the distressed assets at a steep discount.
3. Korea's economy is deeply entangled with the memory business. Companies like SK Hynix built Korea into a manufacturing powerhouse for chips, and that same concentration made its stock market especially vulnerable — the market reportedly fell more than 33% in July, a decline worse than the crashes of 1997 and 2015.
4. Deregulation reshaped modern finance in ways still being felt today. The conversation traces a line from Glass-Steagall's separation of commercial and investment banking, through its effective rollback via the Gramm-Leach-Bliley Act, to today's financial holding companies (like JPMorgan owning both Chase and an investment bank) — blurring risk in ways that echo the AI/memory borrowing spiral.
5. National tech ecosystems don't automatically follow American patterns. Japan's PC industry, dominated by NTT, never fully converged on the IBM-clone standard the U.S. market did, and Korea's cultural relationship to gaming and digital life (partly shaped by heavy state investment in nationwide internet infrastructure) diverged sharply from its neighbors, despite a shared heritage.
6. China's relationship with open-source AI is shifting. Having initially embraced open source partly because it seemed easier to control than proprietary Western tech, China now appears increasingly concerned about having lost that control and is working to reassert it.
7. Distributed, interoperable messaging protocols are having a moment. Tools like Matrix and Nostr (open-source alternatives gaining traction partly in response to Meta's restrictions) revive a decades-old dream of software systems messaging each other freely, distinguishing distributed "pub-sub" models from federated ones where all participants must cooperate.

In this episode of the Stewart Squared podcast, hosts Stewart Alsop and Stewart Alsop II dig into Apple's rocky iOS 27 rollout and the Apple Intelligence features that still don't quite work, before spiraling into Apple's org chart and headcount, the lost art of building apps, chip design and the Apple 100, open source versus closed source (MLX, the Linux kernel, GitHub vs. GitLab), Claude Code and why Anthropic's terminal-first approach might be winning the AI race, the commoditization debate around Chinese open source models, Adobe's fall from grace and the Postscript-to-Flash saga with Steve Jobs, real-time publishing and the Ben Thompson model for podcasting, and manufacturing hardware from PCBs to Sonos speakers.

Timestamps

00:00 — iOS 27 rollout and buggy Apple Intelligence features frustrate both hosts.
05:00 — Debating Apple's headcount, retail vs. corporate split, and designers fleeing to OpenAI.
10:00 — The Apple 100, the blurry line between research and development, and Xerox PARC.
15:00 — Apple's custom chips, open source roots like the Linux kernel and MLX.
20:00 — GitHub origins, Microsoft's enterprise mentality, and life since MS-DOS.
25:00 — Claude Code, Boris Cherny, and why terminal agents are reshaping coding.
30:00 — AI's text-based limits, Chinese open source models, and a coming robotics interview in Japan.
35:00 — GitLab vs. GitHub, what a workbench and compiling actually mean, and Mac performance gripes.
40:00 — Postscript vs. TypeScript, the Courier font, and early PageMaker newsletters.
45:00 — Hot type, the printing press, and why neither host went into industrial robotics.
50:00 — Editing Marine Business magazine and watching Japan and China take over manufacturing.
55:00 — Building PCBs, lessons from Sonos, and pitching a pay-to-listen real-time model.

Key Insights

1. Apple's biggest weakness isn't hardware or chips—it's software. Despite two years of hype, Apple Intelligence still creates duplicate calendar events and can't recognize things already scheduled, revealing a company that excels at silicon and operating systems but consistently ships mediocre apps, a gap the hosts trace back to Tim Cook's leadership.
2. Apple's culture runs on a quiet meritocracy called the "Apple 100," a Steve Jobs-era concept where influence isn't tied to title—a junior engineer can be as pivotal as an executive, which explains how the company sustains innovation despite a bloated headcount of roughly 166,000, nearly half of it in retail.
3. Anthropic's edge may not be model quality alone but its decision to build Claude Code around the terminal, treating programming as just another form of text prediction. This bet, credited largely to Boris Cherny, let Anthropic reach developers directly rather than waiting for polished consumer products.
4. The commoditization narrative around AI cuts both ways. As Chinese open-source models close the gap with American closed-source ones, it either means nobody can maintain a lasting lead, or—as one host argues—the opposite: that leaders become nearly impossible to catch once compounding advantages set in.
5. Adobe's arc from a lean systems company to what one host calls a fallen giant shows what happens when a company loses its performance-driven roots. Built on Postscript and page-description technology for the LaserWriter, Adobe eventually prioritized cross-platform reach over speed, echoing Apple's own struggles with app quality.
6. Real-time publishing is emerging as a business model, not just a technical curiosity. Drawing on Ben Thompson's subscription-driven podcast network, the hosts float charging listeners for live access, turning the current ten-day publishing delay from a limitation into a monetizable feature.
7. Manufacturing know-how doesn't transfer easily across domains. Lessons from Sonos scaling hardware in China, and earlier stories from Mercury Marine's engine factories, show that going from prototype to mass production—especially with physical components like PCBs and speakers—demands specialized expertise that even seasoned tech investors admit they lack.

In this episode of Stewart Squared, Stewart Alsop III sits down with his father and co-host Stewart Alsop II for a wide-ranging conversation that jumps from Apple's iOS 27 preview beta and the long road to Apple Intelligence, to the trust gap between Anthropic and OpenAI and the rise of digital-twin apps like Sentience, before pivoting into venture capital territory with a candid look at Andoril, Palmer Luckey, and the defense-tech boom reshaping how the primes do business; from there the two work through the surveillance creep of modern police tech, China's near-peer standing against the U.S. and its own reusable-rocket ambitions, and finally land on the state of fintech trust, the SpaceX IPO, and how thirty years of early-stage deal-making stack up against today's AI-driven venture landscape.

Timestamps

00:05:00 — Apple Intelligence and the iOS 27 beta merge AI with hardware for an always-on assistant vision.
00:10:00 — Anthropic vs. OpenAI trust, Mira Murati's open-source push, and the Sentience digital-twin app.
00:15:00 — Apple's on-device security compared against Google and Microsoft.
00:20:00 — Tech billionaire philanthropy and legacy: Gates, Zuckerberg, and Jobs.
00:25:00 — Andoril and the personal story of investing alongside Palmer Luckey.
00:30:00 — History of defense-tech venture capital and Andoril's government ties.
00:35:00 — Palantir expanding into Argentina and the rise of predictive policing.
00:40:00 — Data, information, and wisdom in AI-driven knowledge management.
00:45:00 — Token minimizing strategy for running Claude and Codex coding agents.
00:50:00 — Fintech trust: Stripe, Venmo, PayPal, and the Panama Papers.
00:55:00 — SpaceX's IPO and public market valuation reflections.
01:00:00 — Reflexivity, Soros, and LLM token economics shaping VC decisions.

Key Insights

1. Apple's iOS 27 beta shows the company finally following through on the Apple Intelligence promise it botched two years ago, and its real advantage isn't the AI itself but that it's fused to hardware holding a user's calendar, messages, and contacts, letting it answer deeply personal questions Google can't match outside its own Pixel devices.
2. Anthropic's positioning as "the Apple of AI" reflects a market increasingly sorting by trust rather than raw capability, with younger users drifting toward open-source alternatives like Mira Murati's newly funded startup, suggesting safety-focused branding alone won't hold loyalty across generations.
3. Apps like Sentience, which build a "digital twin" by ingesting years of email, messages, and calendar history, hint at where personal AI is headed, but the gap between their mobile and desktop functionality shows this category is still early and unevenly built.
4. Venture capital has quietly become the primary funder of military innovation, with firms like Founders Fund turning early bets on companies such as Andoril and Palantir into a broader industry rush toward defense and dual-use technology after decades of stagnant, cost-plus contracting among the traditional prime contractors.
5. Predictive policing tools are reinforcing existing patterns rather than improving outcomes, since they're trained on historical data that sends more patrols into already over-policed neighborhoods, raising questions about transparency as governments adopt surveillance faster than citizens can question it.
6. The venture capital game has shifted dramatically from early-stage bets to massive growth-equity checks, with average valuations jumping roughly ninetyfold over a decade as AI and defense deals now routinely reach into the billions, leaving classic early-stage investors feeling sidelined by their own industry.
7. As AI agents multiply, the real competitive edge is shifting from model performance to token efficiency, with a "token minimizing" approach using multiple coding agents in parallel emerging as a practical way to build software at scale without hitting rate limits or runaway costs.

In this episode of the Stewart Squared podcast, host Stewart Alsop and guest Stewart Alsop II dive into Meta's latest AI model releases and their broader issues with user addiction, touching on the European Commission's warnings about addictive features and massive fines totaling $1.4 trillion from US state attorney generals. The conversation ranges from Meta's Meta Super Intelligence Lab and their attempts to catch up to OpenAI and Anthropic, to the impossibility of governments controlling AI development as countries rush to build sovereign models. They discuss NVIDIA's open source robotics models, debate the future of humanoid versus non-humanoid robots, and compare the business approaches of Mark Zuckerberg and Elon Musk. The episode also covers Trump's floating ideas about restricting state-of-the-art AI models to US citizens, China's similar restrictions, SpaceX's recent IPO performance, and the concept of shareholder capitalism as applied to government investments in tech companies like Intel and potentially OpenAI.

Timestamps

00:00 Meta releases new AI model and thought-reading technology while facing trillion-dollar fines from state attorneys general for social media harm, particularly to young people
05:00 Discussion of Meta's superintelligence lab attempting to catch up with OpenAI and Anthropic, while their cash-harvesting social media business funds AI development despite past VR failures
10:00 Government inability to regulate fast-moving AI technology, with Trump and China floating ideas about restricting state-of-the-art models to citizens only
15:00 Examining how AI's addictive nature combined with existential fears creates political volatility, plus NVIDIA's open-source robotics models becoming viable alternatives
20:00 Debate over humanoid versus non-humanoid robots, discussing industrial applications and questioning whether humanoid design makes practical sense for factories or homes
25:00 Comparing Elon Musk's technical accomplishments at Tesla and SpaceX with Zuckerberg's social media empire, noting Facebook's real-time scaling innovation happened decades ago
30:00 SpaceX AI IPO analysis predicting failure if stock drops below offering price, plus discussion of shareholder capitalism and Trump's government investment strategy
35:00 Reflecting on information overload in the AI age making it impossible to understand complexity, with neither Trump nor technological developments being predictable anymore

Key Insights

1. Meta faces massive legal liability for its addictive social media practices, with state attorney generals demanding approximately 1.4 trillion dollars in total penalties, including a New Mexico jury awarding 375 million dollars in civil penalties and the state separately seeking 2.7 billion dollars in abatement costs. The European Commission has warned Meta about continued use of addictive features, though any meaningful fine would need to be extraordinarily large given Meta's 2 trillion dollar valuation and substantial cash flow. Despite these legal challenges, Meta continues to harvest cash at an astonishing rate from Instagram, Facebook, and Threads by addicting users without regard for their wellbeing, using that revenue to fund their artificial intelligence initiatives after wasting money on virtual reality.
2. Meta's artificial intelligence efforts through their Meta Super Intelligence Lab have been mixed, with their initial LAMA 4 model considered a disaster, but some internal evaluations suggest their upcoming release could potentially help them catch up to OpenAI and Anthropic, possibly even displacing Google as the third accepted foundation model. The key difference between Meta and competitors like OpenAI and Anthropic is that Meta has enormous cash flow from their social media properties to support their AI development, allowing them to spend freely even if they waste money, whereas OpenAI and Anthropic only generate revenue from their AI products. However, there remains skepticism about whether Meta can truly catch up once having fallen behind in the competitive landscape of artificial intelligence development.
3. Governments are fundamentally irrelevant in controlling artificial intelligence development because technology moves too fast for governmental bodies to understand or regulate effectively. Both Trump and China have floated ideas about restricting state-of-the-art AI models to their respective citizens, but these efforts cannot succeed because AI models are infinitely copyable and open source models are becoming increasingly powerful. The reality is that Pandora's box is already open with AI technology, and as countries realize they don't want dependence on China or the United States, they will develop their own sovereign models, creating a mushroom effect that makes control impossible regardless of what governments attempt to mandate or regulate.
4. NVIDIA is becoming increasingly important in the open source AI model space, particularly for robotics applications, as they develop small open source models that can run inside robots without requiring NVIDIA to monetize the models directly since they profit from hardware sales. Jensen Huang has publicly stated that robotics represents the next major innovation, leading NVIDIA to focus on developing CPUs alongside GPUs and integrated systems with small models for physical AI applications. This represents a significant shift where developers no longer need to rely solely on Chinese models, as NVIDIA's open source offerings are becoming genuinely competitive and useful for specialized applications like machine learning cameras and embedded robotics systems.
5. The definition and future of robotics remains highly contested, with significant debate between those advocating for humanoid robots versus non-humanoid specialized robots, and the Wall Street Journal recently published analysis suggesting humanoid robots may not be the optimal path forward. Tesla has been successful partly because they integrated industrial robots from the beginning rather than hand-building cars, reducing production costs substantially, though their humanoid robot demonstrations have not yet resulted in actual factory deployment despite ambitious forecasts. The challenge with humanoid robots includes safety concerns like a hundred-pound robot potentially killing a child if it falls, and the complexity of replicating human capabilities like hands, though companies like Neo recently claimed to have developed hands that work better than humans.
6. The comparison between Mark Zuckerberg and Elon Musk reveals stark differences in technical accomplishment, with Zuckerberg's primary innovation being real-time scaling for billions of users achieved around 2007, after which Facebook has largely exploited that technology to extract money without meaningful additional innovation. In contrast, Elon Musk has accomplished multiple extraordinary technical achievements simultaneously including getting people to buy Teslas, building factories for cars and batteries, changing the distribution system to bypass dealers, building an electric charging network, and creating SpaceX and Starlink. While Musk may be crazy and hard to like, he has genuinely accomplished substantial technical innovations across multiple domains, whereas Zuckerberg has primarily focused on corrupting youth and harvesting data for the past sixteen years.
7. The SpaceX AI initial public offering illustrates important dynamics about public market trust and company valuation, with shares issued at 135 dollars now trading around 145 dollars after initially rising but falling back near the offering price, and predictions suggest it may fall below the offering price before lockup periods expire. The float representing publicly traded shares is only about five percent of total shares, and when more shares become available ...

In this episode of the Stewart Squared podcast, host Stewart Alsop sits down with his father, guest Stewart Alsop II, to tackle a wide range of tech topics from AI chip design to cybersecurity vulnerabilities. The conversation covers OpenAI's Jalapeno chip (trained by AI in just nine months), the emerging etched.com platform, and Cloudflare's recent power move against Google, while Stewart shares his experience building real-time games on video calls and experimenting with ESP32 hardware for robotic projects. The discussion also dives into Meta's controversial KYC (know your customer) requirements that got Stewart kicked off Facebook and Instagram, Apple's evolution from hardware company to trusted computing partner under Tim Cook's leadership, the security implications of Chinese-manufactured ESP32 chips, and why hardware-focused companies struggle to adopt AI-driven development practices like using LLMs to eliminate software bugs—all wrapped up with an AI fact-check of their previous episode's claims.

Timestamps

00:00 Stewart welcomes listeners and mentions his father's return from travels while he's been enjoying winter in Buenos Aires, setting up discussion topics including etched.com, OpenAI's Jalapeno chip training, and Cloudflare's competitive moves against Google
05:00 Discussion shifts to Meta's controversial KYC implementation and Supreme Court decisions allowing Facebook to require identity verification, with Stewart expressing strong opposition to Meta's practices and considering abandoning their platforms except WhatsApp
10:00 Conversation explores Mark Zuckerberg's personality and Meta's toxic culture, comparing their approach to Apple's user-protective stance and examining how tech companies handle personal data and privacy differently across their platforms
15:00 Deep dive into Apple's historical positioning as user-friendly company under Steve Jobs and Tim Cook, discussing their discipline in product management and how they've maintained consumer trust through consistent privacy protection over decades
20:00 Exploration of hardware complexity and software challenges, including Stewart's robotics workshop using ESP32 microcontrollers where even experienced engineers struggled with basic connectivity issues highlighting system complexity
25:00 Analysis of why hardware companies struggle adopting AI solutions, discussing Apple's bug management approach and questioning why they don't leverage tools like Anthropic's Mythos for eliminating persistent software bugs systematically
30:00 Security architecture discussion focusing on Apple's Unix-based kernel foundation inherited from NeXT, explaining how Avie Tevanian built security into macOS from the beginning making Apple relatively breach-free compared to competitors
35:00 Linux and Unix history explored, examining open source security models and discussing ESP32 operating systems, revealing that FreeRTOS provides embedded operating system functionality for these Chinese-manufactured development boards
40:00 Chinese semiconductor company Espressif discussion, examining potential vulnerabilities in using Chinese hardware while distinguishing between chip-level security and application-layer data access risks in connected devices
45:00 Device Authority company case study about remote device validation and firmware updates, connecting to historical cyberattacks like Stuxnet virus that physically infected Iranian centrifuges without internet connectivity
50:00 Cybersecurity industry overview mentioning Israeli company Check Point as pioneering firm, emphasizing importance of hiring specialized security experts rather than attempting DIY cybersecurity for critical business applications
55:00 Fact-checking segment reviewing previous episode claims about Dario Amodei's credentials, NVIDIA founding dates, software patents, OpenAI's Jalapeno chip timeline, and Waymo's highway incidents with minor corrections noted throughout discussion

Key Insights

1. Apple has maintained user trust through a fundamental alignment with individual privacy rather than corporate interests. Unlike companies such as Meta and Microsoft, Apple has built its brand on protecting user data and maintaining security at the operating system level. This cultural commitment, formalized under Tim Cook but rooted in Steve Jobs' vision, has given Apple a distinct competitive advantage with over 2.5 billion users who feel the company is genuinely on their side rather than exploiting them for advertising revenue or data harvesting.
2. Meta is conducting controversial Know Your Customer verification processes that may represent a troubling expansion of identification requirements for social media platforms. Following what appears to be a 2025 Supreme Court decision, Facebook and Instagram are implementing KYC protocols previously reserved for financial institutions, potentially to legally collect identifying information for AI model training. This practice has driven some users to abandon Meta platforms entirely, viewing it as an unacceptable intrusion that violates the original spirit of personal computing.
3. Hardware-focused companies struggle to adopt AI coding tools because their engineering culture emphasizes control and deterministic systems. Companies like Apple, despite their technical sophistication, remain slow to implement AI solutions for tasks like bug elimination because their hardware-oriented workforce consists of control-oriented engineers uncomfortable with the probabilistic nature of AI systems. This cultural resistance prevents them from fully leveraging tools that could theoretically eliminate persistent software bugs that have plagued their ecosystem for years.
4. Security architecture fundamentally differs between operating systems, with Unix-based systems maintaining inherent advantages. Apple's security strength derives from the Unix kernel inherited from NeXT in 1997, which was designed with security as a core principle. This foundation underlies all Apple operating systems today, from macOS to iOS. In contrast, Microsoft's Windows has never achieved comparable security, making it constantly vulnerable to exploitation despite being the standard for government systems, which represents a significant ongoing risk.
5. The Chinese technology ecosystem, particularly in embedded systems and semiconductors, presents complex security considerations that are more political than technical. Companies like Espressif, which manufactures the ESP32 microcontroller chips, are headquartered in Shanghai and dominate the affordable IoT device market. However, because much of this technology uses open source software like Linux, the actual security risks are less about the hardware itself and more about higher-level software implementations that could potentially access personal data, making concerns somewhat overblown outside of China's Great Firewall.
6. The transition from traditional software development to AI-assisted coding is democratizing hardware prototyping in unprecedented ways. Non-engineers can now successfully program microcontrollers and build functional robotic systems using AI coding assistants, while ironically, experienced electrical and software engineers sometimes struggle with the same tasks due to their ingrained approaches. This represents a fundamental shift in who can participate in hardware development, though it also introduces new considerations around security and trustworthiness of the resulting systems.
7. Modern cybersecurity remains a constant race between attackers and defenders who possess equivalent knowledge and capabilities. The distinction between white hat and black hat hackers is merely one of intention rather than skill, as both groups operate with the same information simultaneously. Historical examples like the Stuxnet attack on Iranian centrifuges demonstrate tha...

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

1. 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.
2. 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.
3. 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.
4. 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.
5. 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.
6. 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.
7. 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.

In this episode of Stewart Squared, Stewart Alsop III sits down with his father, Stewart Alsop II, for a wide-ranging conversation that moves from a heartfelt tribute to the late Brent Schlender — legendary tech journalist and author of Becoming Steve Jobs — through the history and philosophy of journalism, the concept of the fourth estate, and what it meant to cover Silicon Valley's biggest names up close. The two also dig into Cold War history, Russia's ambiguous relationship with the West, the Alsop family's own journalism legacy, and how AI is reshaping the way we think about memory, personal data, and the historical record. For more on Brent Schlender's work, check out his book Becoming Steve Jobs and Stewart Alsop II's Substack obituary for Brent, where he also shared the iconic Fortune magazine cover featuring Steve Jobs and Bill Gates together.

Timestamps

0:00 Brent’s memorial and the jet lag opening, then into who Brent was and his role in tech journalism
5:00 Brent’s journalism background, friendship with major tech figures, and the idea of the three Steves in Steve Jobs’ story
10:00 Why journalists usually stay objective, what the fourth estate means, and how the press acts as a check on power
15:00 The press, patriotism, Cold War context, CIA tensions, and how journalists like Stuart and his uncle navigated American loyalty
20:00 McCarthyism, fear, false accusations, and how brave reporting protected people and challenged demagoguery
25:00 Russia as part West / East, Christianity, borders of identity, and the discussion shifting into Russian history
30:00 Soviet-era travel, tech speeches, old-school publishing, and the problem of reconstructing the past without a digital trail
35:00 History vs. journalism, archives, memory, and why preserving records matters for telling the story later
40:00 Brent’s memorial memories, the Steve Jobs book, and how Brent’s work shaped the industry through insight and relationships

Key Insights

1. 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.
2. 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.
3. 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.
4. 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.
5. 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.
6. 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.
7. 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.

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.