What is the 7-month doubling rule mentioned in this skill?

The 7-month doubling rule is a strategic heuristic suggesting that the 'task horizon'—the length and complexity of a task an AI can reliably complete—roughly doubles every seven months based on current scaling trajectories.

How does this skill help with product development timing?

It provides a framework to categorize features: ship immediately if reliable, build now for marginal performance to catch the next model wave, or wait if the capability is currently non-existent.

What is the 'Steam Engine Mistake' in AI integration?

It refers to the error of simply swapping a human for an AI in an existing process. This skill teaches how to redesign entire systems around AI capabilities, similar to how factories were redesigned for electricity.

Can this skill help if my model performance seems to have plateaued?

Yes, it includes a diagnostic framework to help you determine if you've hit a fundamental scaling limit or if the issue lies in training implementation, data quality, or prompting methodology.

AI Scaling Laws Strategy

Name: AI Scaling Laws Strategy
Author: jona

byjona

0•

数据科学与机器学习

Provides strategic guidance on AI scaling laws, capability trajectories, and product positioning for frontier models.

This skill provides a comprehensive strategic framework based on AI scaling laws to help developers and founders navigate the rapidly evolving AI landscape. It offers specialized guidance on forecasting capability improvements using the 7-month doubling rule, distinguishing between pretraining and reinforcement learning phases, and determining optimal product ship dates. By applying the Amodei-inspired framework, users can avoid the 'Steam Engine Mistake' by redesigning systems for AI-native workflows rather than simple replacement, ensuring products remain competitive as model capabilities advance.

主要功能

01Strategic frameworks for timing product launches against model generations

02Analysis of pretraining vs. reinforcement learning scaling phases

030 GitHub stars

04AI capability forecasting using the 7-month task horizon doubling rule

05Diagnostic tools for identifying implementation errors vs. fundamental scaling limits

06Architectural guidance for building AI-native systems and workflows

使用场景

01Estimating when AI will be capable of handling multi-day or multi-week complex research tasks

02Planning product roadmaps for features that are marginal on current models but viable on future ones

03Strategizing how to redesign business processes to leverage AI's cross-domain synthesis strengths

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add jona/ycombinator-skills ai-scaling-laws-amodei

For use in Claude.ai and ChatGPT

Download Skill