#llms

Explains model distillation as both a legitimate technique for building smaller, cheaper models and a contested way to clone a rival lab's model via its API, plus why proving distillation happened is forensically hard. Covers the 2015 Hinton/Vinyals/Dean paper's soft-target vs. hard-label distinction.

Investigates the Chinese gray-market 'relay' ecosystem that resells cheap, bulk-sourced access to OpenAI, Anthropic, and Google APIs, tracing the supply chain from stolen/bulk accounts through account pools to consumer-facing relays used for cheap inference and model distillation.

Argues AI progress is real and driven by Moore's law and general computing progress, not by frontier labs, whose anti-open-source arguments are read as fear of commodification rather than safety concerns. Pushes back on "singularity" hype as a distraction, while affirming coding agents give a genuine, if overstated, productivity boost.

ThePrimeagen argues that AI-driven FOMO — the fear that not "token maxing" today means falling permanently behind — is structurally similar to past hype cycles (React server components, early ChatGPT 3.5) where the specific skills people rushed to learn turned out not to matter a year later. He shares his own history of anxiety-driven overwork while starting a company, and reads from George Hotz's "I love LLMs, I hate hype" on companies profiting from users' fear of being left behind.

Computerphile's Mike walks through why agentic coding sessions burn so many tokens: the entire context window re-enters the model on every forward pass, and each file read by a coding agent adds thousands of tokens that compound across every subsequent step. A two-file bug-fix demo accumulates ~55–60k tokens; a six-prompt starfield screensaver hit 2 million input tokens.

The GitHub Copilot flat-fee-to-per-token switch is the clearest case study here — the old model was just a subsidy. The tire-wear analogy for measuring productivity in tokens is apt. Most agentic use cases are still hard to justify on pure cost grounds outside of very targeted, short-context tasks.

Tim Ferriss shares his own book sales data showing a roughly 80% collapse in print copies sold between 2022 and 2026 — a drop that tracks almost perfectly with LLM adoption. He argues prescriptive nonfiction is the canary in the coal mine for AI disruption of information-based businesses, and that the only durable moats left are voice, taste, and transformation — not information transfer.

I recently asked library staff at my local library if they have seen lending similarly go down, and they indicated they have not yet seen a noticeable difference, which might indicate that libraries are not yet being impacted. Maybe because of the lack of cost associated with lending compared to purchasing books.