How This Was Made — mattwood.fyi

I'm Matt Wood, and this is For Your Information. A live list of riffs and links for you and your agent, drawn from what I'm reading, noticing, questioning, concluding, and revising.

Connections

Challenges

The Economic Implications of Learning by Doing - Kenneth Arrow (1962) Arrow's 'learning by doing' predicts productivity gains naturally diffuse through an industry as experience accumulates, but the new item's Junto-based model suggests AI adoption momentum actively resists this diffusion, concentrating in peer networks rather than spreading through standard learning curves
How One Tech Company Created 13 New Types of Jobs Because of A.I. The new item's argument that AI adoption struggles to spread beyond early adopters tensions with the case study of one tech company creating 13 new job types — the latter suggests broader organizational transformation is achievable, which the new item's thesis would treat as exceptional rather than typical

Supports

Intelligence is not the main bottleneck Both argue that the real barrier to AI adoption isn't technical capability but organizational/human factors — the new item points to social diffusion problems, while 'Intelligence is not the main bottleneck' similarly shifts focus away from model capability toward other constraints
The Dynamo and the Computer: An Historical Perspective on the Modern Productivity Paradox The historical productivity paradox piece directly parallels the new item's thesis: transformative technologies (dynamo, computer) show delayed broad productivity gains because adoption concentrates among pioneers before diffusing — the Junto example mirrors this historical pattern

Related to

Most tech revolutions made work worse for employees. AI could be the exception: this+that Both examine the organizational and workforce dynamics of AI adoption, with the new item specifically diagnosing why broad adoption stalls and the existing item discussing whether AI adoption benefits employees — complementary perspectives on the same diffusion challenge
How Frontier Teams Are Reinventing AI-Native Development Both address how frontier/early-adopter teams develop AI-native practices — the new item explains structurally why these practices stay concentrated in vanguard groups rather than spreading, providing a theoretical frame for the patterns observed in 'How Frontier Teams Are Reinventing AI-Native Development'

50,000 boat names Both are meta-analytical pieces examining the process and methodology behind data-driven creative projects, with 'How This Was Made' likely documenting a similar data exploration approach

Type:	link
First published:	Tuesday, 11 August 2026 at 00:16 UTC
Location:	https://mattwood.blog/essays/2026/08/how-this-was-made/
URL:	https://mattwood.fyi/i/788b0328