---
title: "Excited to announce my new book, AI Economics: Building and Scaling…"
type: post
date: 2026-08-22
source: linkedin
original_url: "https://www.linkedin.com/feed/update/urn%3Ali%3Ashare%3A7496804969778606080"
topics: ["ai-economics", "agentic-ai", "ai"]
summary: "Excited to announce my new book, AI Economics: Building and Scaling Intelligence. AI is getting cheaper, more capable and increasingly autonomous. But there is a question I believe matters far more: What value are we actually creating with all this…"
draft: false
---

Excited to announce my new book, AI Economics: Building and Scaling Intelligence.

AI is getting cheaper, more capable and increasingly autonomous.

But there is a question I believe matters far more:

What value are we actually creating with all this intelligence?

Today, much of AI economics is measured at the point of consumption: tokens, API calls, GPU hours, model costs and infrastructure spend.

These measures tell us what AI consumed.

They don't tell us what AI accomplished.

That is the starting point for AI Economics.

I believe the economic unit of AI needs to move closer to the reason we deploy AI:

Compute → Output → Tasks → Successful Tasks → Outcomes → Value

And as we move from models and copilots toward agents and reusable skills, differentiation moves upward:

Model → Capability → Skill → Domain → Industry → Enterprise

The ability to encode domain knowledge, industry expertise and enterprise context into reusable, executable intelligence could become an important source of competitive advantage.

But AI economics cannot stop at financial cost.

AI has a physical economy. Intelligence ultimately depends on infrastructure, hardware, energy, water and materials.

We therefore need holistic AI unit economics spanning finance, infrastructure and physical sustainability.

Not simply what AI costs, but what outcome it creates, what value that outcome delivers, and what resources were required to deliver it.

This leads us toward measures such as cost per outcome, energy per outcome, carbon per outcome and value per unit of resource, alongside absolute consumption.

There is also one industry where I believe this shift will be particularly profound: IT services.

For decades, much of the industry has scaled through:

People × Utilisation × Rate = Revenue

But what happens when expertise becomes executable?

When agents perform work and skills encode reusable expertise, the delivery model starts moving toward:

People + Agents + Skills + Platforms + Domain Expertise → Outcomes

This has implications for billable hours, offshore delivery, talent pyramids, managed services, pricing, margins, intellectual property and outcome-based contracts.

I dedicate a part of the book to this transition and what an AI-native services firm could look like.

AI isn't simply changing how IT services firms deliver work. It could change what they sell, how they price it, how they scale expertise, and where they capture value.

AI Economics brings these ideas together by connecting intelligence, skills, outcomes, resources and value into one economic system.

As intelligence becomes increasingly abundant, the question becomes:

What should we use intelligence for, and what value can we create with it?

📘 AI Economics: Building and Scaling Intelligence is now available in paperback on Amazon worldwide. Link in the comments section.