#research

Recap of a Linear Digressions conversation with Stanford linguist Chris Potts on "invisible failure modes" — quiet moments in human-AI conversations where something goes wrong (self-contradiction, answering the wrong question, silent give-up loops) and the user never notices. Covers his research finding these in a majority of studied conversations, the novice/delegative vs. expert/augmentative user stance divide, evidence that confident-sounding model language anti-correlates with correctness yet correlates with trust, and the "seven levels of enlightenment" the researchers went through when they found the labeling task itself too hard without help from frontier models.

Temperament matters more than talent in AI research — a meditation on the daily practice of reading and building, and why equanimity is the real prerequisite.

The Zen framing isn't just a metaphor — he quotes Suzuki directly, structures the piece like numbered koans, and the equanimity point is genuinely it: sit with failure the same way you sit with success, neither attached to the outcome.

Why scaling up multi-agent AI systems doesn't deliver proportional benefits — collaboration turns out to be a distinct capability, and adding agents to sequential tasks often makes things worse.

Strong on coordination cost — agents communicating and handing off work isn't free, and that overhead often swamps any gains from parallelism.