ICYMI 2026-08-01: IA Problem; AI Costume

Our weekly roundup of signals from the AI noise, for humans leading change.

Bootstrapping your intelligence stack
Last week, I shared a WSJ article that said businesses are realizing they needn’t blow their budgets on frontier AI. What’s the alternative? A mix of models. Some tasks require cleverer (and more expensive) intelligence than others. How do you decide which models to apply to which tasks? My latest post for Unfinishe Thoughts explains.

IA is foundational for AI
Patrick Neeman argues for something I’ve said for at least the past two years: information architecture is the foundation for good AI implementations. AI is leading organizations to rediscover the value of IA, at long last. Patrick names me as part of IA’s history, but the AI challenges in this piece are what my practice focuses on now.

What’s really happening to jobs?
A recent report from Stanford concludes that claims about AI impact on the job market may be overstated. There are two ways of understanding AI: as a “normal technology” that will change the world over time (“transformative but gradual”) or as an unprecedented disruption with immediate world-shaking implications. I’m firmly in the former camp: you should invest in AI smartly, with a look to the long term.

Stateless MCP
An important upgrade this week: the Model Context Protocol (MCP) maintainers announced version 2.0 of the spec. These are Simon Willison’s notes on the release, which go deep into tech details. My read for business leaders: this version is more secure and therefore better suited to enterprise applications than agentic coding harnesses (e.g., Codex, Claude Code), at least for operational workflows.

How Swatch saved the Swiss watch industry
A fascinating case study of an industry (apparently) facing technological disruption. Much of what I thought I knew about the near-death and restoration of the Swiss watch industry was wrong. Cheap Japanese quartz watches weren’t the culprit. What almost killed the industry were protectionist policies and bad incentives. What does this have to do with AI? Tech disruption was the coup de grâce that almost flatlined the patient, but there were deeper structural issues. How ready is your industry/organization to compete in our time of technological disruption?