616: Nvidia Dethrones Apple at TSMC, Coase Conjecture and AI Lab Profits, Boots on the Ground vs Panopticon, BART Applied Economics, Apple’s 2025 Scorecard, Uranium, and Catchlights

2024-01-12 · Source: Liberty’s Highlights · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering, Emerging Technologies & Innovation · Depth: Intermediate, long

Summary

Apple's long-standing position as TSMC's top client has ended, with Nvidia surpassing it in 2025. Apple spent an estimated $132 billion USD at TSMC since 2014, peaking at 26% of TSMC's revenue in 2021, but falling to 17% last year compared to Nvidia's 19%. This shift highlights the increasing demand for chips in AI. The article also discusses the potential for AI to centralize political power by collapsing the "human layer" of accountability, contrasting it with historical checks on power like whistleblowers. Furthermore, it covers BART's successful implementation of 72-inch tall fare gates, which increased annual revenue by $10 million and reduced "corrective maintenance" by 95%. Apple's 2025 scorecard reveals strong iPhone, Mac, and iPad performance but significant criticism for OS quality and Apple app quality, particularly regarding MacOS 26 Tahoe's "Liquid Glass" design. The new Apple Studio Display offers only minor updates, while the M5 Pro and Max chips feature a "Fusion Architecture" with up to 18 CPU cores and 40 GPU cores, aggressively tuned for performance over battery life, claiming up to 8x faster AI workloads than M1 Max. The U.S. nuclear comeback faces a fuel problem due to rising demand and a ban on Russian imports, with Centrus Energy racing to build enrichment capacity to meet a $2.3 billion backlog. Finally, the Coase Conjecture is applied to AI pricing, suggesting open source models might help frontier labs maintain higher prices by segmenting the market.

Key takeaway

For CTOs and VPs of Engineering evaluating AI strategy, consider the long-term implications of AI on accountability and power structures, not just technical capabilities. Your teams should actively engage in policy discussions around AI governance and ethical deployment to safeguard against unintended societal consequences, especially concerning surveillance and autonomous systems. Additionally, assess how open-source AI models might influence your procurement and pricing strategies for frontier AI services.

Key insights

Active curiosity and high agency are crucial for original thought and avoiding information overload.

Principles

• Friction of implementation historically checks state power.
• Open source can segment markets, aiding frontier pricing.
• Design quality is paramount for user experience.

Method

To foster original outputs, curate attention (active curiosity) by seeking variety and quality in information inputs, rather than passively consuming content.

In practice

• Implement tall fare gates to reduce maintenance costs.
• Prioritize software quality over new features in product development.

Topics

• AI Governance
• Apple Silicon
• Semiconductor Manufacturing
• Open-Source AI
• Nuclear Energy Supply

Best for: CTO, VP of Engineering/Data, Director of AI/ML, General Interest, Investor, Business Analyst

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Editorial summary, takeaway, and curation by AIssential. Original article published by Liberty’s Highlights.