---
schema: 1
type: term
title: '**capability overhang**'
description: >-
  **Capability overhang**: the gap between what AI models can already do and
  what products, protocols, and infrastructure have actually absorbed — latent
  capabilities that exist in the models but have not yet been wired into real
  workflows and tooling.
canonical: >-
  https://liquidgold.cc/channel/everyinc/explore/term/capability-overhang-06cd9464e1f8
markdown: >-
  https://liquidgold.cc/channel/everyinc/explore/term/capability-overhang-06cd9464e1f8.md
llms: null
parent: 'https://liquidgold.cc/channel/everyinc/llms.txt'
channel: everyinc
updated: '2026-10-23T22:12:35.346Z'
language: null
children: []
---


# \*\*capability overhang\*\*

Canonical: https://liquidgold.cc/channel/everyinc/explore/term/capability-overhang-06cd9464e1f8

Parent: [Channel index](https://liquidgold.cc/channel/everyinc/llms.txt)

## Definition

**Capability overhang**: the gap between what AI models can already do and what products, protocols, and infrastructure have actually absorbed — latent capabilities that exist in the models but have not yet been wired into real workflows and tooling.

## Capability overhang **Capability overhang** is the gap between what frontier AI models can already do and what products, protocols, and organizations have actually absorbed. When a new model ships with abilities that no one has wired into real workflows yet, those capabilities sit unused — *overhanging* the current product surface — until infrastructure, interfaces, and integration work catch up. In this channel's material, the term belongs to the same conversation as the **agentic web**: Microsoft CTO **Kevin Scott** argues the interesting frontier is no longer scaling laws but what happens when models' existing capabilities get connected to the tools and data of the internet. [^385ed583-4107-5c5a-b47a-33e1dde27a3b@327]  ## How the term is used here The overhang shows up concretely across the channel's interviews: - **Models can act, but the plumbing is missing.** Scott frames usefulness as agents that **take action on your behalf** — using tools and reaching APIs — which requires an ecosystem where existing sites and APIs are plumbed through for agents, not capabilities the models lack. [^eea0a522-b6b8-5472-bf62-08b0a42ab8a3@268] - **Protocols close the gap.** **MCP** as the HTTP of the agentic web and **NLWeb** as its HTML are presented as the infrastructure needed to turn latent model capability into reachable, composable action. [^eea0a522-b6b8-5472-bf62-08b0a42ab8a3@326] - **Product design, not model quality, is the bottleneck** in much of this material: MCP servers need real product management, evals, and careful context-budget design before models' abilities translate into production agents. Use the term the way this channel does: as an explanation for why **capability** and **deployment** move on different clocks.  - **Capability overhang** = model capabilities that already exist but have not yet been exposed by products, protocols, or infrastructure. - The channel connects it to the **agentic web**: closing the overhang means building protocols like MCP and NLWeb, not training smarter models. - It explains why the hard work is product and infrastructure design — context budgets, evals, sandboxes — rather than raw model improvement.