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!)