Posts

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:

• 80% fewer tokens
• Fewer tool calls
• Lower latency
• Less compute
• Lower cost
• Reduced energy consumption …isn’t just more efficient. It’s more skilled.

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

Tomorrow, I’ll be speaking at Google I/O Connect, Bengaluru on a topic I’ve become deeply passionate about: Lean Agentic AI.

We’re entering an era where organizations will deploy not one or two agents—but thousands. The question is no longer: can we build them?

It’s: can we design agents that are efficient by design — across cost, energy, and carbon?

In this session, I’ll show how to engineer production-ready agentic systems that optimize across three currencies that compound simultaneously:
💰 Dollars — reducing operational cost
⚡ Watts — improving energy efficiency
🌍 Carbon — attaching a real gCO₂e number to every agent task using the Green Software Foundation’s Software Carbon Intensity (SCI) ISO specification.

Every wasted token costs money, watts, and emissions at once.

But carbon is only part of the story. As AI adoption accelerates, so do its impacts on water consumption, e-waste, and grid demand.

We’ll also explore why measurement is the missing layer—connecting cost, energy, carbon, water, and waste into a single engineering discipline so we can optimize what actually matters.

We’ll walk through practical architectural patterns on the Google Gemini Enterprise Agent Platform: model selection, orchestration, observability, evaluation, simulation, memory, and carbon-aware execution.

The goal is not just measurement—it’s continuous reduction of cost, energy, carbon, water, and waste.

If you’re attending Google I/O Connect, I’d love for you to join me at Community Lounge 1. And if you’re around afterwards, please come and say hello. I’d love to chat about Agentic AI, Green Software, or whatever you’re building.

More than anything, I’d love to see our developer community start treating cost and environmental impact as first-class engineering metrics.

A big thank you to Paul Ravindranath G , Rajat Bhatia, and the Google I/O Connect team for the invitation.

See you there!

#GoogleIO #GoogleIOConnect #BuildWithGemini #GoogleCloud #GeminiEnterprise #Gemini #AgenticAI #LeanAgenticAI #GreenSoftware #AIEngineering #Sustainability #FinOps #SoftwareEngineering Green Software Foundation

🚨 AI is advancing faster than your organization can transform.

That may be the single biggest reason why so many enterprises are struggling to demonstrate ROI from Agentic AI.

Every month, we see smarter models, better reasoning, lower costs, and more capable agents. Yet many business leaders are still asking the same question:

We’ve invested in AI… so where’s the business value?

The problem isn’t that AI isn’t ready.
The problem is that AI evolves at machine speed, while organizations transform at human speed.

In the latest edition of Technology Bytes, I explore what I call The Transformation Gap—the growing distance between rapidly evolving AI capabilities and the slower pace of organizational change.

The article also explores why:

• Agentic AI introduces a new economic model built on tokenomics, where development, testing, and innovation all consume compute.
• Enterprises are no longer managing static software—they’re managing continuously evolving intelligence.
• Benchmark scores don’t create business value. Redesigned business processes do.
• The organizations that achieve the greatest ROI won’t necessarily deploy the smartest agents—they’ll redesign how work gets done.

The future of enterprise AI isn’t just about building better models.
It’s about building organizations that can evolve alongside them.

📖 Read the latest Technology Bytes: Agentic AI ROI — Why Most Organizations Aren’t Seeing Business Value Yet

I’d love to hear your perspective:

What’s the biggest barrier to realizing ROI from Agentic AI today—technology, governance, organizational change, or something else?

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 merge triggered a full eval sweep. The judge model was pricier than the model being judged. Prompt sweeps ran untracked, most discarded within the hour.

Meter. Gate. Sample. Substitute. The ladder that turns development inference from an unbudgeted reflex into a governed line on the bill.

Read the full issue ↓

Green Software Foundation #greenai #efficentai #aieconomics #leanagenticai

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 embedding-model upgrades — each a drop-in change on the API side — had quietly become one of the largest non-production AI infrastructure expenses on the account that year. Nobody had put re-embedding on the upgrade review.

Inventory. Tag. Retire. Reuse. The ladder that turns AI-scale storage from an accident into a decision.

Read the full issue ↓

Green Software Foundation #greenai #efficentai #leanagentciai #aieconomics

AI doesn’t need another breakthrough model.
It needs a return to engineering fundamentals.

Somewhere between foundation models and agentic AI, we started confusing capability with architecture. Bigger models, more agents, longer context windows, and increasingly complex workflows aren’t always better engineering.

In this edition of Technology Bytes, I explore why the next competitive advantage in AI may not come from building more intelligent systems—but from building better-engineered ones.

And why it’s time to move from the “reel economy” of impressive AI demos to the real economy of measurable business outcomes.

📖 Read the full article below.

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 tool schema in the catalog, whether the tool is needed or not. Few-shot examples fixing behaviours long since stabilised. Formatting rules. Safety framing. The user’s question is at the bottom.

The prompt the model reads is not the prompt the user wrote. And the difference between the two is what shows up on the bill.

Four rungs inside: Inventory, Trim, Split, Amortise.

Read the full issue in today’s edition

Green Software Foundation #greenai #efficentai #aieconomics #leanagenticai