Spiky, not smooth When people talk about AI capabilities, they often picture a smooth, evenly rising curve of progress. The spiky frontier is a way of saying the reality is anything but smooth: models are extraordinary at some things and surprisingly weak at others, so the edge of what AI can do looks like a jagged skyline rather than a gentle slope. [^01a0f15a-8ad2-74f5-9d59-7bdcf79e28df@23] [^01a0f15a-8ad2-74f5-9d59-7bdcf79e28df@44] How it’s used in this material In the channel’s material, the term appears while weighing whether a model like Claude Mythos — reportedly too dangerous for Anthropic to release — is as alarming as it sounds. Dan Shipper argues the fear is overblown, and the spiky frontier is part of that argument: a headline capability (“this model can do X, therefore it’s too dangerous”) doesn’t tell you how reliable, general, or usable that capability actually is. Understanding the spikes — where a model excels and where it falls short — is what keeps the panic in perspective. [^01a0f15a-8ad2-74f5-9d59-7bdcf79e28df@123] [^01a0f15a-8ad2-74f5-9d59-7bdcf79e28df@190] [^01a0f15a-8ad2-74f5-9d59-7bdcf79e28df@291] Why the framing matters - Headline capabilities mislead. A single scary-sounding skill doesn’t mean uniform competence. - Unevenness is the honest picture. Exploring the actual contour of model abilities helps you understand what a model can and can’t be trusted with. - Riding the frontier is practical. Once you know where the spikes are, model progress becomes something you can put to work rather than something to fear. [^01a0f15a-8ad2-74f5-9d59-7bdcf79e28df@23] [^01a0f15a-8ad2-74f5-9d59-7bdcf79e28df@291]