---
title: The goal of AI should be to make the next problem easier, not the…
type: post
date: 2026-09-11
source: linkedin
original_url: "https://www.linkedin.com/feed/update/urn%3Ali%3Ashare%3A7504212984513228800"
topics: ["agentic-ai", "ai", "ai-economics"]
summary: "The goal of AI should be to make the next problem easier, not the same problem more expensive. That idea became the foundation of my new book: Reusable Intelligence Beyond Inference: Building AI Systems That Learn When Not to Reason AI is becoming remarkably…"
draft: false
---

The goal of AI should be to make the next problem easier, not the same problem more expensive.

That idea became the foundation of my new book:  
Reusable Intelligence  
Beyond Inference: Building AI Systems That Learn When Not to Reason

AI is becoming remarkably good at reasoning.

But as AI moves into agents, enterprise workflows and everyday operations, I believe we need to ask a different question:  
Why should a system keep paying to rediscover what it already knows?

Humans do not reason from scratch every time.  
We:  
Think → Learn → Remember → Reuse → Notice change → Think again

AI systems should be able to mature in the same way.

In the book, I introduce several ideas around this:
- Learning Dividend: experience should make future work easier
- Reasoning Budget: spend inference where uncertainty actually remains
- Reuse Boundary: know not only what can be reused, but where reuse stops
- Selective Reasoning: Known → Reuse. Unknown → Reason. Uncertain → Escalate.

The bigger idea is simple:

The first era of AI scale was about making intelligence available on demand.  
The next era will be about making intelligence accumulate.

Because the most advanced AI system may not be the one that reasons the most.  
It may be the one that knows when not to reason.

Reusable Intelligence is now available on Amazon.

If you are building AI agents, enterprise AI platforms, or thinking about the economics of AI at scale, I hope this gives you a different lens for what comes next.