Frontier models will eventually eat all the tedious tailored add-ons as just part of something they can do.

Right now models have roughly all of the written knowledge available to mankind, minus some obscure held out private archives and so on. They have excellent skills and general abilities to construct plausible sequences of actions to accomplish work, but we need to hold their hands to really get decent performance across a wide range of activities. Skills and agent frameworks and MCP carve out different domains of that problem, with successful solutions providing training data for future models that might be able to be either generalized, or they'll be able to create a vast mountain of synthetic data following successful patterns, and make the next generation of models incredibly useful for a huge number of tasks, by default.

It might also be possible that by studying the problem, identifying where mode collapses and issues with training prevent the right sort of generalization, they might tweak the architecture and be able to solve the deficiency through normal training runs, and thereby discard the need for all the bespoke artisanal agent specifications.

His point, I believe, was that it is early in the innovation cycle and they very well be replaced quickely with different solutions/paradigms.

Adoption on most of these has been weak, except MCP (and whatever flavor of markdown file you like to add to your agent context)