---
schema: 1
type: term
title: Frontier models
description: >-
  **Frontier models** are the latest, most capable AI models available at a
  given moment. In this channel's material, the term carries a specific set of
  practical implications for builders [^e780a42f-b16e-5a92-b18c-685de50ce822@0]:
  - **Design for two years out.** The advice is to build app-layer products
  using the latest frontier models now, rather than obsessing over current
  inference costs — AI costs are expected to plummet, so ride that improvement
  rather than optimizing for today's economics. - **Small user bases make this
  viable.** A small team can ship features powered by frontier models that a
  company serving billions physically can't roll out, and can afford to run them
  expensively at small scale while users are few. - **They reward the expensive
  middle period.** Part of why AI products are costly today is frontier-model
  economics — but the material treats that as survivable with the right capital,
  because the winners will be defined before costs fall. - **They shape what you
  fix.** Since frontier models keep improving, effort spent solving problems the
  next model version will solve has a short shelf life. See the related topic
  for how this frontier-model mindset fits into finding a sticky core
  interaction, specialization for professionals, and augmenting judgment rather
  than replacing it.
canonical: >-
  https://liquidgold.cc/channel/everyinc/explore/term/frontier-models-0bd7de7f114a
markdown: >-
  https://liquidgold.cc/channel/everyinc/explore/term/frontier-models-0bd7de7f114a.md
llms: null
parent: 'https://liquidgold.cc/channel/everyinc/llms.txt'
channel: everyinc
updated: '2026-10-23T22:12:35.346Z'
language: null
children: []
---


# Frontier models

Canonical: https://liquidgold.cc/channel/everyinc/explore/term/frontier-models-0bd7de7f114a

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

## Definition

**Frontier models** are the latest, most capable AI models available at a given moment. In this channel's material, the term carries a specific set of practical implications for builders [^e780a42f-b16e-5a92-b18c-685de50ce822@0]: - **Design for two years out.** The advice is to build app-layer products using the latest frontier models now, rather than obsessing over current inference costs — AI costs are expected to plummet, so ride that improvement rather than optimizing for today's economics. - **Small user bases make this viable.** A small team can ship features powered by frontier models that a company serving billions physically can't roll out, and can afford to run them expensively at small scale while users are few. - **They reward the expensive middle period.** Part of why AI products are costly today is frontier-model economics — but the material treats that as survivable with the right capital, because the winners will be defined before costs fall. - **They shape what you fix.** Since frontier models keep improving, effort spent solving problems the next model version will solve has a short shelf life. See the related topic for how this frontier-model mindset fits into finding a sticky core interaction, specialization for professionals, and augmenting judgment rather than replacing it.

**Frontier models** appears in this channel's material as a practical building decision, not just a label for the biggest AI systems. In the conversation with Granola's Chris Pedregal, frontier models are the latest and most capable AI models available at a given moment — and the material explores what they mean for how app-layer builders should design, fund, and ship products: small teams can afford to run them expensively, startups can ship features powered by them that giants physically can't roll out at scale, and the right move is to build for the product two years out while trusting that inference costs will plummet [^e780a42f-b16e-5a92-b18c-685de50ce822@0]. Use this entry to explore how that frontier-model mindset connects to Granola's bets on volatile AI economics, startup advantages over big tech, specialization, and augmentation over agents.