Thank you to our member organizations, and especially to the leads and teams from Amadeus, AVEVA, Schneider Electric, and Siemens, for applying the SCI for AI specification to real-world AI use cases.
This is exactly how a specification becomes truly useful: through community participation, practical implementation, shared learning, and continuous refinement. Together, we can turn measurement into action and make sustainable AI the norm. Green Software Foundation
AI can make your organization more intelligent while making it less knowledgeable.
That sounds contradictory.
But as AI starts doing more of the analysis, reasoning, and decision support, something subtle begins to shift:
The capability remains.
The understanding behind that capability starts moving elsewhere.
We have spent decades learning how to manage technical debt.
Now enterprises may need to manage a new kind of debt:
AI Intelligence Debt.
The issue is not whether we should outsource intelligence to AI. We should, where it creates value.
The real question is:
Which intelligence can we safely outsource, and which intelligence must the organization continue to own?
In the latest edition of Technology Bytes, I explore:
Beyond Technical Debt: The Rise of AI Intelligence Debt
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 that differ by orders of magnitude, purely because the interfaces around them ask for different things.
A regenerate button that made sense against a weaker model. A model picker that defaults to the largest tier. A chat surface shipping the full session history on every turn. Dashboard summaries generated on cards nobody views.
Measure. Intent-align. Defer. Revisit. The ladder.
Read the full issue ↓
Green Software Foundation #greenai #efficentai #aieconomics #leanagenticai #ai
Before we build the next AI data center, we should answer 8 questions.
Not after construction.
Not in the next sustainability report.
Before the shovel goes into the ground.
1️⃣ How much water will it use — and from where?
2️⃣ How much electricity will it require — and who pays for the grid upgrades?
3️⃣ Can it turn down when the grid is under stress?
4️⃣ Whose land is being used — and who is affected?
5️⃣ What’s next door — a river, aquifer, forest, or neighborhood?
6️⃣ What are the public incentives worth per job actually created?
7️⃣ Who checks the commitments — and how often?
8️⃣ What happens if the data center closes? Who pays for cleanup?
These aren’t complicated questions.
But today, they aren’t always asked together, consistently, and early enough.
And that’s a problem.
AI data centers are becoming some of the largest infrastructure projects of the AI era.
They consume enormous amounts of electricity.
They require cooling.
They can consume significant amounts of water.
They occupy land.
And they become part of the communities and infrastructure around them.
This isn’t an argument against building them.
We need the infrastructure to power the future of AI.
But we need a better contract between AI infrastructure and the communities that host it.
That’s why in my latest episode of Agentic AI — the future of intelligent systems, I propose an AI Siting Ledger — a shared eight-question framework that puts the numbers, commitments, and accountability on the table before construction begins.
Because sustainability reporting tells us what happened.
The AI Siting Ledger asks what will happen — before we approve it.
And as agentic AI becomes more autonomous and persistent, the demand for compute will only grow.
The future of intelligence isn’t just a software problem.
It is a physical infrastructure problem.
🎙️ I’ve explored this in my latest podcast episode:
The AI Data Center Problem — What Does Intelligence Take?
👉 Spotify podcast link in the description below.
What would you add to the eight questions?
#AgenticAI #AIInfrastructure #DataCenters #GreenAI #Sustainability #AI Green Software Foundation
If you’ve ever felt like the world is moving faster than you can think —
Keep reading.
If you’ve sat in a meeting about “AI transformation” while quietly wondering what it all actually means for you —
Keep reading.
If you’ve built a life that looks impressive on LinkedIn but feels restless at 5 AM —
Keep reading.
If you’ve ever wanted to stop running, sit on a bench, and ask yourself one honest question —
What am I actually running toward?
Then this story was written for you.
6:17 AM.
Two strangers. One bench. A park by the sea in Mumbai.
Every morning at exactly 6:17, a man runs past. He builds AI systems for a living. His life is fast, optimized, and impressive on paper.
Every morning at exactly 6:17, a woman is already sitting there. She photographs sunrises for a living. Her life is slow, uncertain, and honest.
One morning, she looks up and asks him a question nobody at work ever has.
That conversation changes everything.
No plot twists.
No villains.
No algorithms.
Just two people figuring out what kind of life is actually worth building — in a world that won’t stop telling them to move faster.
I wrote this novel because I believe the most important question of the AI age isn’t what machines can do.
It’s what humans choose to do with the time, space, and freedom that technology creates.
6:17 AM is available for FREE on Amazon for the next few days.
It’s short. You can finish it in one sitting.
Maybe on your morning commute.
Maybe before your first meeting.
Or maybe tomorrow at 6:17 AM.
Link to the book - https://amzn.to/4ggnFbD
Link in comments 👇
The token model doesn’t tell us what AI accomplished.
It tells us what AI consumed.
Tokens.
Compute.
Inference.
Context.
But consumption isn’t the same as value.
An AI system can consume 100,000 tokens and still produce nothing useful.
Another can consume 10,000 tokens and successfully complete the same task.
Yet our AI economics often starts and ends with:
How much did it cost?
That’s the Meter Problem.
Think about a taxi.
The driver takes a wrong turn.
Then another.
Then a third.
The meter keeps running.
You wouldn’t accept paying extra because the driver kept taking wrong turns.
Yet that’s effectively what happens with AI.
You refine the prompt.
Add more context.
Retry.
Ask it to reason again.
The meter keeps running.
And with Agentic AI, the problem can multiply:
Reason → Tool call → Fail → Retry → Delegate → Reason again
Every step consumes resources.
So the question shouldn’t just be:
How much does a token cost?
It should be:
How much useful work are we getting from the tokens we consume?
Because:
More tokens ≠ More value.
More reasoning ≠ Better outcomes.
More iterations ≠ Greater efficiency.
The next evolution of AI economics isn’t just cheaper tokens.
It’s more useful outcomes per unit of compute.
That’s the thinking behind Lean Agentic AI. (https://leanagenticai.com/)
Intelligence measured by output.
Efficiency built by design.
What if AI is making you more capable—but not smarter?
We’ve spent the last few years learning how to get AI to produce more.
More code.
More content.
More analysis.
More decisions.
But there’s a question we’re not asking enough:
Who owns the thinking?
In the latest edition of Technology Bytes, I explore Cognitive Ownership: The AI Literacy We’re Not Teaching.
I introduce three ways we can work with AI:
🔹 AI as a Tool — Human thinks → AI accelerates
🔹 AI as a Collaborator — Human thinks ↔ AI challenges
🔹 AI as a Substitute — AI thinks → Human approves
The third may be the most productive-looking—and the one we need to examine most carefully.
Because when AI can produce an answer faster than we can think through the problem, a new gap can emerge:
The Thinking Gap — the distance between what AI can produce and what we can independently understand, defend, and modify.
And as we move toward agentic AI, that gap could matter even more.
The question isn’t whether AI makes us smarter.
The question is: What do we still own when AI does the thinking?
Read the latest Technology Bytes and tell me:
Are you using AI to think better—or to think less?
The biggest AI risk isn’t your technology. It’s your leadership message.
Listen carefully to how executive teams talk about AI.
👉 “AI changes everything.
👉 “AI will make us 20% faster.
👉 “Adopt AI or get left behind.
👉 “Responsible AI must come first.
👉 “We need better AI models.
👉 “Show me the ROI.
👉 “We must build AI capability across the organization.
Most leadership messages optimize for a single dimension of AI—capability building, speed, productivity, governance, technology, or ROI. The challenge isn’t that they’re wrong. It’s that they’re incomplete.
AI transformation requires all of these dimensions—but not all at the same time. The role of leadership isn’t to repeat one message. It’s to know which message the organization needs next.
A leadership message doesn’t just describe an AI strategy.
It becomes the AI strategy.
If the message is productivity, teams optimize today’s workflows instead of designing tomorrow’s business.
If the message is governance, risk management can overshadow experimentation.
If the message is technology, organizations underinvest in culture, skills, and operating model change.
If the message is immediate ROI, long-term capability takes a back seat to short-term wins.
The organizations that succeed in the AI era won’t necessarily have the biggest models or the largest budgets.
They’ll be the ones whose leadership message creates the behaviors needed for transformation.
Because people don’t adopt technology.
They adopt the story their leaders tell.
So before asking,
What is our AI strategy?
Ask a more important question:
What story are we telling about AI—and what behaviors is that story creating?
That answer may determine whether your AI journey succeeds… or quietly stalls.
How is your organization currently framing its AI journey?
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 platform did before it became a commodity.
Linux.
Apache.
Docker.
Kubernetes.
The foundation always becomes cheaper.
The value always moves higher.
I believe AI is entering that same transition.
The next competitive advantage won’t be the model.
It won’t even be the agent.
It will be AI Skills—and ultimately, the business value they create.
In this edition of Technology Bytes, I introduce a framework I believe will shape the next era of AI:
Token Economy → Skill Economy → Value Economy
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. Call it the Dataset Receipt.
A healthcare ML team fine-tuned a base model with careful discipline. Then the sustainability review asked what it took to produce the training data itself. Labels at scale. Synthetic samples generated by another model. Dataset sprawl across nine places.
Source. Scope. Synthesize with Care. Archive and Retire. The ladder.
Read the full issue ↓
Green Software Foundation #greenai #agenticai #efficentai #aieconomics
From Zero Trust to Zero Assumption for Agentic AI systems.
I believe this is the next evolution of enterprise security for Agentic AI.
Zero Trust transformed security by ensuring that no identity is trusted by default. But autonomous AI systems introduce a different challenge. An agent can authenticate successfully, operate within its permissions, and still make a sequence of decisions that creates business risk.
The question is no longer just:
Who is making the request?
It’s also:
Should this autonomous system perform this action, in this context, at this moment?
In the latest Technology Bytes, I introduce Zero Assumption—a proposed extension to Zero Trust where every autonomous decision must continuously earn trust before it is allowed to execute.
The article explores why the enterprise security perimeter is shifting from APIs to autonomous workflows, what an Agent Security layer should look like, and why Lean Agentic AI can improve not only efficiency but security as well.
If you’re designing, deploying, or governing AI agents, I’d love to hear your thoughts on whether this architectural direction resonates with you.
📖 Read the full newsletter.
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 carrying it ever since. A product team shipped a feature backed by three models. A foundation model borrowed at scale. An open-weights model adopted freely. A fine-tuned classifier paid in full. Three receipts, three amortization stories.
Attribute. Reuse. Right-size. Amortize. The ladder that turns training footprint from an off-book cost into a governed one.
Read the full issue ↓
Green Software Foundation #greenai #efficentai #aieconomics #leanagenticai
Sustainability advances faster when governments, industry, and innovators move together.
It’s great to see the CleanEnviro Summit Singapore highlights video now live. Thank you to the CleanEnviro Summit Singapore team for featuring highlights from my keynote.
It was wonderful to be part of the conversations on how innovation, AI, and sustainability are coming together to shape the future of Environmental Services, supported by Singapore’s strong government leadership.
One message I hoped to leave with the audience is that as AI becomes part of every industry, we need to build systems that are not only intelligent, but also sustainable, measurable, and responsible.
Looking forward to seeing the momentum continue into CESG 2027.
#CESG #Sustainability #GreenAI #GreenSoftware #EnvironmentalServices Green Software Foundation
HR for AI Agents?
It sounds unusual today.
In a few years, it may become a standard enterprise function.
As organizations deploy hundreds—or even thousands—of AI agents, managing them won’t be just an engineering problem. It will become a management discipline.
In my latest Technology Bytes article, I introduce Agent Resource Management (ARM)—a framework for governing, measuring, and optimizing AI agents at enterprise scale.
Every enterprise investing in Agentic AI should be thinking about this now.
Read the full article.
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, and it changed the shape of the decision.
The outgoing fleet retired at half its useful life. The incoming fleet carried a higher embodied footprint. The three-year refresh cadence had never actually been chosen. And the same decision produces opposite answers in different regions.
Measure. Extend. Match. Choose. The ladder that turns hardware refresh from a procurement default into an embodied-carbon decision.
Read the full issue ↓
#greenai #efficentai #leanagenticai #aieconomics Green Software Foundation
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 external reviewer flagged the gap. The report covered what the system drew. It missed what the system cost to exist — the GPUs’ manufacturing footprint, the training runs on the company’s own infrastructure, the emissions embedded in the facility itself.
Name. Attribute. Amortize. Report. The ladder that widens the AI emissions boundary from operational to honest.
Read the full issue ↓
#greenai #efficentai #aieconomics #leanagenaticai Green Software Foundation
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 about why efficiency should be a first-class AI skill — packaged, taught, and installable — not an afterthought bolted on after deployment.
Today, I’m turning that idea into practice.
Introducing Lean Agentic AI Skills.
An open-source suite of 49 agent skills for reducing cost, carbon, energy, and complexity — across your web, cloud, data, and AI stack.
The skills work across Claude, Codex, Copilot, Antigravity, Cursor, and every AI coding agent that has adopted the format. Free. MIT licensed. No lock-in.
Inside the article, I also share a bigger idea:
AI Skills are the new Design Patterns.
The 1990s gave developers design patterns. The 2000s gave us frameworks. The 2010s gave us cloud patterns. The 2020s will be defined by reusable, installable expertise — for AI agents instead of developers.
Read the full edition ↓
I’d love to hear what you think — and even more, what skills our community builds next.
#leanagenticai #aiefficency #skills #agenticai #ai #aieconomics #greensoftware Green Software Foundation
AI Skill = Capability × Efficiency
Today’s AI agents are measured by what they can do.
Can they search?
Can they reason?
Can they write code?
Can they use tools?
Can they collaborate with other agents?
But as enterprises move from deploying a handful of agents to orchestrating thousands, capability alone is no longer enough.
An AI agent that delivers the same business outcome with:
Perhaps it’s time to redefine what we mean by an AI skill.
AI Skill = Capability × Efficiency
Efficiency shouldn’t be treated as an optimization after deployment. It should be taught as a first-class skill—enabling agents to know when to use a smaller model, when to stop reasoning, when to reuse existing knowledge, and when another tool call simply adds cost without adding value.
In my latest Technology Bytes, I explore why the future of Agentic AI won’t belong to agents that think the longest—it will belong to agents that know when they have thought enough.
I’d love to hear your perspective.
Should efficiency become a first-class AI skill, measured alongside accuracy and reasoning?
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, cache — two-thirds of the standing capacity had been warm and idle for the entire month.
The autoscale floor set too high at launch. The vector index over-configured for peak queries that never came. The pilot deployment nobody retired.
Size. Share. Cycle. Retire. The ladder that turns standing AI infrastructure from a fixed cost into a managed one.
Read the full issue ↓
Green Software Foundation #greenai #leanagenticai #aieconomics #efficentai #genai
It was a pleasure speaking at the Google I/O event on Lean Agentic AI.
As organizations move from deploying a handful of AI agents to thousands, efficiency can no longer be an afterthought. We need to design agentic systems that are efficient by design—across cost, energy, and carbon.
One of the most rewarding parts was the discussion with developers after the session. Many hadn’t considered that every architectural decision in an AI system has implications beyond performance—it also affects cost, energy, and carbon. Raising that awareness is important because sustainability isn’t just about reducing environmental impact; it’s also one of the most effective ways to lower the total operational cost of AI. That’s how we make sustainable AI the new engineering norm.
As part of the Green Software Foundation (GSF), I’m also excited about the progress we’re making with the SCI for AI standard. Rather than focusing on offsets, SCI for AI provides a standardized way to measure, compare, and continuously reduce the carbon impact of AI workloads. The emphasis is simple: measure what matters, then engineer for reduction.
Many attendees requested the slides and demo after the session, so I’ve made them available on GitHub:
https://lnkd.in/dFJkcNTp
I’d love to hear your thoughts and continue the conversation on building lean, efficient, and sustainable agentic AI.
#GoogleIO #GoogleIOConnect #GoogleCloud #AgenticAI #LeanAgenticAI #GreenSoftware #SustainableAI #Gemini #SoftwareEngineering Green Software Foundation