ai economics
24 pieces on ai economics, across articles, books, talks, and videos.
For decades we designed software to last because building it was expensive. AI is changing the cost of creation but not the cost of verification. If generating software becomes cheaper than trusting it, the reusable asset may no longer be the code — it may be the confidence we can carry forward.
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…
My LinkedIn feed is full of one question: "Has AGI arrived? In the latest edition of Technology Bytes, I ask a different one: If AI is now intelligent enough for us to debate AGI, can it solve a simpler problem for itself? Can AI become cheaper to run, lower…
Building a powerful AI model is one thing. Making an enterprise depend on it is another. AI research is moving fast, but enterprise delivery is not keeping pace. A model can look exceptional in a benchmark and still be difficult to deploy reliably across real…
The token economics model is not scalable. AI economics needs to change. Today, every time AI reasons, the meter runs. That is acceptable when the problem is new, uncertain or exceptional. But once the enterprise has already solved the problem, why should it…
Most enterprises do not need AI to think every time the business runs. Yet that is increasingly how we are designing enterprise AI. A large part of business execution may begin with discovery, ambiguity and reasoning. But once the right approach is understood…
Why are organizations paying AI to relearn what they already know? Much of enterprise AI today follows the same pattern: A transaction arrives. An LLM reasons. An outcome is produced. Then the next similar transaction arrives, and the LLM reasons all over…
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…
Day 25 showed that the interface between a user and a model decides how much inference gets called. Today closes the arc at the newest interface in the stack — the one between agents — where fan-out, escalation, accumulated state, and loose contracts compound into a bill no single team owns end to end.
Day 25 of Green, Efficient AI is live: The Interface Sets the Bill. A user creates intent. The interface translates that intent into inference demand. The model produces the bill. Two products can wrap the same underlying model and produce inference bills…
The Token Economy has an expiration date. Not because AI is slowing down. Because intelligence itself is becoming infrastructure. For the past three years, we've optimized prompts, tokens, latency, and inference costs. That's exactly what every technology…
Day 24 of Green, Efficient AI is live: The Data That Trained It. Every AI model remembers the data that trained it. Few teams remember the footprint of producing that data. The most expensive part of a training project can happen before training ever starts.…
Day 23 of Green, Efficient AI is live: The Model Keeps Its Environmental Receipts. Every model in production carries a receipt for what it cost to exist. Training was a paid event. The footprint closed the moment the run finished, and the model has been…
Day 22 of Green, Efficient AI is live: The Silicon in the Rack. A cloud-native platform team approved a hardware refresh. Newer accelerators. Better performance per watt. Clean operational case. The sustainability lead asked one question before signing off,…
Day 21 of Green, Efficient AI is live — The Footprint Already Spent. A financial services company published its AI carbon report. Every inference call metered. Every kilowatt-hour tracked. Grid intensity factored in by region. The number looked clean. An…
49 new efficient skills for your AI agent. The kind that spot waste — before your cloud bill or carbon footprint does. The latest edition of Technology Bytes is live — and it comes with something you can actually use, starting today. Last article, I wrote…
Day 20 of Green, Efficient AI is live — The Capacity That Waits. A retail team ran per-call optimizations across their assistant for months. The bill barely moved. When they pulled utilization data across the full stack — model endpoints, vector database,…
Day 19 of Green, Efficient AI is live — The Test Tail. A team spent six months bringing production AI costs down. The playbook worked — production inference became dramatically cheaper. The monthly bill barely moved. The gap was hiding in plain sight. Every…
Day 18 of Green, Efficient AI is live — The Forever Data. Every AI system carries two footprints. The work it performs, and the data it leaves behind. The second one rarely gets named. A B2B platform running a retrieval assistant found that three routine…
Day 17 of Green, Efficient AI is live — The Invisible Payload. A user asks a support agent to reset their password — seven words, roughly ten tokens. The trace shows the model actually receives thousands of tokens. A system prompt written months ago. Every…
Day 16 of Green, Efficient AI is live — Every Guardrail Is a Call. The most overlooked line item in an AI system is the safety wrap. So why do most teams run four or five model-based checks on every request without ever costing them? In many production…
Day 14 of Green, Efficient AI is live — The Grid You Didn't Choose. Most production AI runs in a region somebody picked years ago, on hardware procured for a different workload, and on schedules chosen for operational convenience rather than carbon…
Day 12 of Green, Efficient AI is live — Cache What Doesn't Change. Depending on the workload, forty to ninety-five percent of every AI prompt is the same as the last one — the system prompt, the tools, the document the agent is reading. The model reprocesses…
Wait… the AI budget is already gone? This is becoming a real conversation inside organizations. Coding assistants. Meeting summaries. Research agents. Copilots. Multi-agent workflows. AI feels effortless… until someone sees the token bill. In early 2025, I…