AI Philosophy
AI Privacy, Strategy, and Human Value: What Matters Beyond the Hype
The most durable AI strategies may not be about building the biggest model. These three pieces focus on privacy, human judgment, and foundational understanding.
Apple Intelligence Is Right on Time
TLDR: Apple’s privacy-first, on-device AI approach is strategically superior to cloud-dependent models. It plays to device-layer structural advantages rather than competing in the chatbot race.
Key Insight: Controlling the device layer and prioritizing privacy may prove more durable than winning the chatbot race.
How I Stopped Worrying About AI and Learned to Value My Humanity
TLDR: The author identifies three “reality gaps” between AI fear narratives and actual usage. Hands-on experience reveals what is irreplaceably human — judgment, taste, and emotional context.
Key Insight: The best antidote to AI anxiety is hands-on use.
A Jargon-Free Explanation of How AI Large Language Models Work
TLDR: The clearest accessible explanation of LLMs available — covering tokenization, word vectors, attention mechanisms, and next-token prediction. No jargon, no hype, just the mechanism.
Key Insight: An LLM is a next-token prediction engine — understanding this explains both fluency and confident nonsense.
What does this mean for how we think about AI?
Understanding what LLMs actually do — predict the next token — is the foundation for every sound AI strategy. From Apple’s privacy-first architecture to individual practitioners overcoming AI anxiety, clarity about the mechanism leads to better decisions than either fear or hype.