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

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

Timestamps

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

Key Insights

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.