Engineers and PMs are becoming "compute allocators"

2026-05-20 · Source: How I AI · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, quick

Summary

The discussion posits a fundamental shift in the roles of engineers and product managers, suggesting they are increasingly becoming "compute allocators." This new responsibility involves making strategic decisions about where and how to spend computational resources, particularly in the context of large language models like Claude. The speakers emphasize that planning documents, such as Product Requirements Documents (PRDs) and specifications, remain crucial for defining the scope and shape of this computational work. The conversation highlights that the cost of running advanced AI models, exemplified by Claude's ability to "spend 500 bucks" for eight hours of operation, directly translates into resource allocation decisions that must be made during the planning and specification phases of product development.

Key takeaway

For AI Product Managers and Engineers designing new features, your role now fundamentally includes compute allocation. You must explicitly define and justify the computational resources required in your PRDs and specifications, understanding that "running Claude for eight hours" translates directly to a significant cost. Prioritize features that offer the highest return on compute investment, and consider using AI models themselves to explore and refine requirements, especially for uncovering unknown unknowns.

Key insights

Engineers and PMs are evolving into compute allocators, prioritizing resource spending for AI models.

Principles

• Planning documents guide compute allocation.
• Compute cost dictates product feasibility.

Method

The method involves defining compute-intensive tasks within specs and PRDs, then iteratively refining requirements by interacting with AI models like Claude to uncover unknown unknowns.

In practice

• Integrate compute cost into PRD requirements.
• Use AI models for requirement discovery.

Topics

• Compute Allocation
• Product Management
• Engineering Roles
• AI Resource Management
• Planning & Specification

Best for: Machine Learning Engineer, NLP Engineer, AI Product Manager, AI Engineer, Product Manager, Director of AI/ML

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Editorial summary, takeaway, and curation by AIssential. Original article published by How I AI.