ICYMI 2026-07-25: Define ‘Best’
Our weekly roundup of signals from the AI noise, for humans leading change.
Corporate America culls AI expenditures
Smart businesses are waking up to the fact that using the latest, greatest AI models for everything isn’t a good investment. Lots of models are good enough for many tasks. The key is designing systems that use the right level of intelligence for the right use cases. My take: models are becoming a commodity; the frontier labs have no moat. (WSJ gift link)
On those new Chinese open weight models
All this talk about optimal model use is spurred by a couple of new Chinese open weight model releases that aren’t far behind the leading (closed) U.S. models. Ben Thompson offers a counterpoint to the item above: he expects the frontier U.S. labs to also dominate the tier below. Either way, I’m leaning toward designing model-agnostic systems.
DoorDash’s AI code reviewer
Details on DoorDash’s AI code evaluator system. Different stages, using different models. That is, a system architected to make optimal use of models with different characteristics (and importantly, costs.) Key line: “‘best’ is meaningless until you say best at what, on which cases, at what cost.” (H/t Benedict Evans)
AI helping (not replacing) workers?
A new Google study reinforces a trend we’ve followed for some time: AI isn’t really replacing workers, but augmenting them. Most people aren’t fully delegating their jobs to AI. Instead, they’re using it as a collaborator that leverages their expertise. Google has a horse in this race, so solve for the balance — but I believe AI job replacement fears are overstated. (WSJ gift link)
Claude Cookbook
I used to love O’Reilly’s “cookbook” series of books, which showed how to solve practical problems with particular technologies. This is like that, but for Claude. A tremendous resource that suggests your organization could benefit from becoming more AI literate. (We can help with that!)