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Day 4 of Green, Efficient AI is live - Make the Call Count Smaller.

The cheapest, fastest, lowest-carbon inference is the one the system never made.

Most teams focus on model cost. Bigger model. Smaller model. Better prompt.

But one of the biggest opportunities in AI efficiency lives somewhere else:

Calls your system never needed to make in the first place.

The same question answered forty thousand times.
The same document fetched again inside an agent loop.
The same retry sent back to the model for a problem the network should have handled.

Major providers already reduce some waste below the API line through prompt caching.

The bigger opportunity lives above it.

Dedupe → Reuse → Carry → Catch

One ladder for reducing unnecessary inference.

The cheapest call is the one you never make.
The second cheapest is the one you make once and remember.

Day 4 of Green, Efficient AI ↓

Green Software Foundation #greenai #efficentai

What if the future of AI becomes too expensive to sustain?
Not because models are costly.

But because intelligence itself becomes something we consume continuously.

We are moving toward a world where AI systems won’t just answer questions.
They will quietly prepare for meetings, coordinate schedules, negotiate prices, summarize research, monitor health patterns, optimize spending, and make decisions in the background.

And many of these systems won’t work alone.
They will reason.
Retrieve memory.
Call tools.
Collaborate with other agents.
Quietly operating throughout the day.

Now multiply this by millions of people.
And perhaps hundreds of agents per person.

This is where a hidden economy begins to emerge.
Tokens.
Often treated as a technical metric.

But perhaps better understood as the currency of intelligence.
Because every token represents computation.

And computation carries consequences:
💸 Rising operational costs
Which increases…
⚡ Energy consumption
Which requires more…
💧 Water for cooling infrastructure
Leading to growing…
🌍 Carbon emissions
And when systems reason unnecessarily, carry oversized context, or overuse tools…
♻️ Waste multiplies.
As Agentic AI scales, the question may no longer be:

How intelligent can AI become?
But:
How intelligently should intelligence operate?

The latest episode of Agentic AI — The Future of Intelligent Systems explores the hidden economy powering Agentic AI — from personal agents and token demand to cost, energy, water, carbon, and the future of efficient intelligence.

🎧 Listen here:
https://lnkd.in/dHJTjYeG

Day 3 of Green, Efficient AI is live — Right-Size the Model.

One principle, written for everyone who builds, funds, designs, operates, or uses AI.

A frontier model summarising a meeting transcript is a Ferrari delivering a pizza.
It works. It also consumes an order of magnitude more energy, water, and cost than a small model that would have done the same job indistinguishably to the end user.

And sometimes the honest answer is not a smaller model. It is no model.

The smallest technology that solves the task is the right one. Not the most powerful. Not the newest. Not the one the demo used.

Green Software Foundation

Day 2 of Green, Efficient AI is live: The Invisible Bill.

Every AI response carries five costs at once — energy, water, carbon, hardware, money. One sits on a dashboard the team already has open. The other four are simply waiting to be brought into view.

What it means: efficient AI begins the moment all five become visible at the point of decision — every query, every retry, every agent loop, every GPU hour.

Who it’s for: every seat at the table — engineering, platform, architecture, sustainability, business. No single role solves efficient AI alone, and every role has something to bring.

Green Software Foundation

Every AI feature ships an invisible bill.
Cost. Energy. Water. Carbon. Hardware.
Most teams see one.

Almost none see the full picture — and that gap is about to define the next era of how we build software.

Today, I am launching a new LinkedIn newsletter:

Green, Efficient AI

Building AI that is intelligent in what it delivers — and efficient in how it runs.
The newsletter is an almost-daily read built around one small, actionable principle at a time.

No long essays. No abstract frameworks.
Just one idea per issue — useful enough to bring into your work tomorrow.

Waste in AI doesn’t live in one place.
It runs through every layer — from the model to the prompt to the hardware itself.

Which means every role has something to contribute:
Engineers across the lifecycle. Architects. Designers. Platform teams. Sustainability leads. Finance partners. End users.
And increasingly, the agents consuming AI on our behalf.

No single role solves efficient AI alone.
That is the conversation this newsletter is for.

Day 1 is live.

It explains why this newsletter exists and how it works.

If you build AI, fund it, design it, operate it — or simply use it every day — I’d love for you to subscribe and read along.

Green Software Foundation

AI can answer faster.
But can it ask better questions?

That may be one of the biggest shifts in the age of AI.

For years, expertise was measured by how quickly answers could be produced. Today, answers are abundant. AI can generate them in seconds.

The differentiator is changing.

The future may belong to people who can:

• Ask better questions
• Connect ideas across domains
• Think critically beyond the obvious
• Apply judgment when certainty is low
• Bring empathy, context, and responsibility to decisions

Perhaps the real challenge is not learning how to compete with AI.
It is learning how to become more than AI.

A recent reflection from my latest book, Be More Than AI: The Reason AI Isn’t Enough:
AI can amplify intelligence.
But meaning, judgment, empathy, and purpose remain deeply human.

In a world where AI becomes increasingly capable, the question shifts from “What can AI do?” to:
What makes humans indispensable?

More thoughts, ideas, and episodes from the book:
📘 Buy the book - https://lnkd.in/dKuU_2Sr
🌐 https://bemorethan.ai/

What is one human capability you believe becomes more valuable as AI advances?

Every token your AI generates has a price tag. Three, actually.

There is the bill — from your model provider, your hyperscaler, or your own inference infrastructure. There is the energy drawn from the grid to produce it. And there is the carbon released into the atmosphere because of it.

For a long time, the assumption was that engineering teams had at least the first one under control. The age of agents has dismantled that assumption.

In the latest edition of Technology Bytes, I unpack why token economics is breaking — and why the answer is not a smaller AI bill, but a different way of thinking about AI altogether.

The next decade of AI will not be won by whoever spends the most on compute. It will be won by whoever wastes the least.

Read the full edition 👇
#GreenSoftware #SustainableAI #TokenEconomics #TechnologyLeadership #AI Green Software Foundation

Last week was my Day 2 at the The Linux Foundation — and I was already at the All Hands meeting.

Our CEO, Jim Zemlin opened the event by reflecting on the culture and values that continue to shape the Linux Foundation and its communities, and one slide in particular stayed with me:

Helpful. Hopeful. Humble.

Those words felt real because they were reflected in the people around me and in the spirit of the open communities we build. It’s about helping others move forward, staying hopeful about our collective future, and remaining humble enough to know that real impact comes through collective effort and shared purpose.

One of those people is Sean Mcilroy. He has been the backbone of the Green Software Foundation projects and operations since the early days. He didn’t wait for me to “onboard” — he immediately started connecting dots, making introductions, and sharing context. That is what community actually looks like.

Swapping Mumbai’s humidity for snow while running on jet lag made the week memorable, but the shared spirit of the community is what grounded me. Having been part of the GSF journey since its inception, seeing the broader Linux Foundation ecosystem up close reinforced why this work matters. Sharing a picture with Sean from the trip.

At the Green Software Foundation, we’re tackling a challenge no single organization can solve alone: building sustainable software and AI systems. As AI adoption accelerates and the token economy scales, the need for standards, measurement, tooling, reduction strategies, and collective responsibility becomes even more important.

But weeks like this remind me that the people here aren’t waiting for permission or applause. They’re just building.

Grateful to be here.

After five impactful years, closing a defining chapter at Accenture.

Five years ago, tech sustainability was still an emerging space. What began as a set of ideas evolved into building awareness, creating assets, platforms, and frameworks, driving real client impact, and contributing to new ISO standards that are shaping how organizations approach sustainability in software and AI.

Over time, this work gained industry recognition, with leadership reflected in evaluations by Forrester and Everest Group.

Grateful to Accenture for the opportunities, and to the Tech Sustainability Innovation team and extended teams who helped build and scale this work.

I’m excited to begin a new chapter with the Green Software Foundation and The Linux Foundation, stepping in as Executive Director.

Having been involved with the Green Software Foundation since its inception, this transition feels both natural and meaningful.

There is still a long way to go—and much more to build together as a community.

Sustainability is a shared responsibility. It requires collaboration across organizations, industries, and communities to make it the default, not the exception, with a clear focus on balancing cost, carbon, and energy in how software and AI are built and operated.

Thankful to Asim Hussain for building a strong foundation, and looking forward to working with the ecosystem to take this forward at scale.

Onward! 🚀

Gadhu Sundaram Jonathan Turnbull Sanjay Podder Carolin Rubner Vinjosh Varghese Yusuke Kobayashi Todd Moore Sean Mcilroy Jamie A Cowan Russell Trow Pindy Bhullar Gosia Fricze Joseph Cook Kirsty Du Plessis Jenya Saprykina Jabari George

AI can process.
AI can generate.
AI can calculate.

But can it think?
Not even close.

Because thinking isn’t one thing. It has layers.
Ask — before you solve, question whether it’s the right problem.
See — connect patterns from your experience, not just data.
Feel — sense what the situation actually needs.
Decide — make the call when there’s no clear answer.
Own — stand behind your thinking.

I call it The Human Stack.

AI operates on the surface.
The depth? That’s entirely yours.

Episode#3 of Be More Than AI is live.

📖 And if you want the full deep dive — I’ve written a book on this. Link in comments.

Children are already growing up with AI.
The real question is — are we teaching them how to use it responsibly?

Little AI Explorer is now live on the iOS App Store:
https://lnkd.in/d24DexEx

This has been a journey of building something simple, yet meaningful — a space where curiosity meets AI in a way that feels natural, safe, and engaging.

Why Little AI Explorer?
AI is becoming part of everyday life.
But one critical piece is often missing — ethics and responsibility.

If children are exposed to AI early, they shouldn’t just learn what it can do. They should understand:

• How to question what AI tells them
• Why not everything generated is correct
• What responsible usage looks like
• Where human judgment still matters

Little AI Explorer is built as an app for kids — designed to:

• Cultivate early curiosity about AI
• Introduce responsible thinking from day one
• Build awareness of ethical AI usage
• Provide a safe space to explore and learn

Because the goal isn’t just early exposure.

It’s building a generation that grows up with AI — thoughtfully, ethically and responsibly.

For more details, visit - https://lnkd.in/dN5E8jta

This is just the beginning.
Would love your thoughts and feedback 🙌

What does Google’s Gemma 4 release actually change for Sustainable AI?

Most conversations will focus on benchmarks.
That’s not where the impact is.
The shift is architectural.

Google DeepMind released Gemma 4 with four open models:
→ E2B and E4B (effective models)
→ 26B MoE (4B active at inference)
→ 31B dense

The smaller models are where this becomes practical.

This is less about model size, and more about where inference happens. Gemma’s “effective parameter” design means:

• Only part of the model is active per request
• Lower memory footprint
• Lower compute per inference

This makes on-device inference viable for a broader set of use cases.

On-device inference improves sustainability under specific conditions:

• High-frequency tasks (e.g., typing assist, voice input)
• Repeated interactions where network calls are avoided
• Models that fit efficiently within device constraints

There are also trade-offs:

• Edge devices can be less efficient per unit of compute than optimized data centres
• Battery consumption shifts energy usage to the device
• Hardware lifecycle impact remains part of the equation

The practical opportunity is in hybrid AI architectures:

1. Edge-first inference (E2B / E4B)
   Handle frequent, low-complexity tasks locally
2. Cloud escalation (larger models like 31B)
   Route only complex queries to higher-capacity models
3. Selective compute (MoE / effective models)
   Activate only the required subset of the model during inference
4. Context-aware routing
   Decide dynamically between edge and cloud based on latency, cost, and energy

Sustainability outcomes are driven by inference strategy, not just model efficiency. The impact comes from:

• Reducing unnecessary large-model calls
• Keeping repetitive workloads closer to the user
• Designing systems that avoid excess compute

Gemma 4 expands the design space.
The sustainability outcome depends on how it is used.

For Gemma 4 details , visit https://lnkd.in/dp7HFbpB

#Gemma4 #SustainableAI #EdgeAI #GreenSoftware #OnDeviceAI #AIEfficiency Green Software Foundation #google

Telling people AI will take their jobs is easy.

It gets attention.
It creates urgency.
It spreads fast.

Because fear sells.
But it also reduces a complex shift into a one-sided story—driven by fear and shallow measures of productivity.

AI is changing work. That part is real.
But it’s not just removing work.
It’s changing what the work actually is.

The part where you write the report? AI can do that.
The part where you analyze the data? AI can do that.
The part where you build the presentation? AI can do that.
The part where you write the code? AI can do that.

But deciding what the report should say,
questioning what the data is missing,
choosing what actually matters,
designing the system, the responsible architecture, the trade-offs—
that part is still yours.

The autopilot work is going away.

What’s left isn’t just “skills.
Thinking.
Seeing.
Feeling.
Deciding.
Owning.
Not tasks. Not outputs.

The things that were always yours.

So the real question isn’t:
Will AI take my job?
It’s:
What part of my work actually requires me?

I wrote this book because something didn’t sit right.
We’ve been measuring human value through output and execution.
AI didn’t remove that value.
It exposed how little we understood it.

This book maps what remains—what humans uniquely bring, why it’s irreplaceable, and how to use AI to your advantage.

I call it The Human Stack:

• Ask More — question what’s worth solving
• See More — expand beyond what’s visible
• Feel More — understand what’s not said
• Decide More — act under uncertainty
• Own More — take responsibility

📘 Be More Than AI
Be the Reason AI Isn’t Enough
Now available on Amazon - https://amzn.to/4v9iiBU

AI will keep evolving.
The Human Stack is not what it replaces. It’s what it reveals.
Own it.

AI doesn’t remove work. It reveals what the real work was all along.

Take something simple — email.
In many roles, a good part of the day goes into:
Reading threads.
Understanding context.
Figuring out what’s needed.
Drafting responses.

Now AI can do most of that in seconds.
And when it does… something interesting happens.
👉 The effort disappears.
👉 But the outcome doesn’t.

Which raises a deeper question:
If AI handles more of the doing…
what does “work” actually become?

🎥 Episode 2 of “Be More Than AI” explores this shift.
Because as tools remove friction,
what remains becomes much clearer.

Technology Bytes NewsLetter has crossed 5000+ subscribers.

Thank you for being part of this journey—your engagement, shares, and conversations are what make this community meaningful.

In the latest edition of Technology Bytes, I explore a shift that is quietly redefining how we think about control in the AI era:

From Data Sovereignty to Decision Sovereignty — Rethinking Sovereignty in the Age of Agentic AI

We’ve spent the last decade asking:
Where should our data live?

But in the age of Agentic AI, a more important question is emerging:
Where is our intelligence allowed to act?

Because when systems begin to make decisions autonomously, sovereignty is no longer about controlling data.
It’s about controlling behavior.

This edition dives into why this shift matters, how agentic systems are changing the boundaries of control, and what it means for leaders designing AI-driven systems at scale.

If you’re thinking about the future of AI, governance, and architecture—this is a conversation worth having.

This software was developed by AI.

A newer, more powerful version of that AI has now taken over.

No problem,” they say. “We have all the history.
Code. Logs. Decisions. Data. Everything is fed into the new AI.
It understands everything. Almost.

Except why the product was built in the first place. What constraints shaped the system. Why certain trade-offs were made.

The thinking is missing.

The system still runs. It even improves itself.
But slowly, it starts drifting.

Optimizing for metrics that were never the goal.
Fixing problems that were never problems.
The dashboard looks perfect. The outcome isn’t.

Then a bug appears. Something the AI has never seen before.
It tries to fix it. Again. And again.

But there is no past to learn from. No pattern to follow.

The organization mistook outputs for capability.
They built a system that could generate code…
but no one who understood what the system was meant to do.

And so a new kind of work emerged.
Not writing code. Not fixing bugs.
Reconstructing intent.

Turns out, you can transfer code.
You can transfer context.
But you can’t transfer understanding.

And that’s where the gap begins —
not between AI and humans,
but between generating software and engineering it.

We’re optimizing everything with AI.
Except the life we’re living.

At 6:17 AM, a runner passes the same bench.

Every day.
Same route.
Same rhythm.

Run.
Work.
Repeat.

Like code—executed without question.

Until one morning…
someone notices.

A quiet observer.

A collector of sunrises.
And a conversation begins.

What starts small…
turns into something deeper.

About work.
Ambition.
And the quiet restlessness many feel today.

Because this moment we’re living in is different.

AI is reshaping everything.
Roles are shifting.
The pace is accelerating.

We’re building systems that:
optimize decisions
reduce friction
move faster than ever

And somewhere along the way—
we’ve started doing the same with our lives.
Optimizing.
Accelerating.
Repeating.

But the questions haven’t gone away.

If anything…
they’ve become louder.

6:17 AM” isn’t a story about AI.
It’s about what it feels like to live through this shift.

The space between progress… and purpose. If you’ve ever:

• looked successful—but felt something missing
• kept moving—but didn’t know why
• wondered if speed has replaced meaning This story might stay with you.

Because before everything begins again at 6:17 AM…
there’s still a choice.

📖 6:17 AM is now available on Amazon: https://lnkd.in/dXv_yuib
Be honest—what are you running toward?

I have open-sourced Lean Agentic AI as Claude Skills.

Agentic systems are rapidly becoming the new application architecture.
They are more capable than ever.But capability without discipline leads to waste — unnecessary compute, unchecked cost, and unmeasured emissions.

• Multiple agents calling multiple models
• Tools invoked repeatedly
• Massive context windows passed around every step

Powerful? Yes.
Efficient? Not really.

Every design decision has a price — in dollars, emissions, and complexity.
Lean Agentic AI is the discipline of paying only what the outcome is worth.

The repository includes practical, working tools such as :
🚀 /cost-analyzer — Scans your code, identifies wasteful LLM calls, and shows exactly how much you’re overspending
🚀 /bloat-detector — Scores your system across 7 common agentic bloat patterns

Instead of building agents that simply do more, the focus shifts to systems that do the right things — with the least possible compute, cost, and carbon impact.

If efficiency is treated as an afterthought, the industry risks repeating the same patterns seen in early cloud adoption — runaway cost, uncontrolled complexity, and unmeasured energy consumption.

The next evolution of AI engineering will not be defined only by smarter agents.
It will be defined by leaner agents.

Repository links are shared in the first comment.

#AgenticAI #AI #LeanAI #GreenAI #AIEngineering #LLM #Sustainability #ClaudeCode Green Software Foundation

I asked AI to think outside the box.
It revealed something interesting about how humans share ideas.

The ideas came instantly.
Some practical.
Some unusual.
Some surprisingly bold
And a few that were clearly less practical.

AI simply kept generating possibilities.
No hesitation.
No overthinking.

And that’s when something interesting became clear.
AI generates ideas freely.
Humans don’t always share them that way.

In most organizations, ideas naturally pass through a few filters before they are spoken:
Hierarchy.
Alignment.
Timing.
Consensus.

By the time an idea reaches the room, it has often already been refined — or quietly dropped.

Not because people lack ideas.
But because ideas are often shaped by context, timing, and group dynamics.

AI doesn’t navigate those dynamics.
It simply generates possibilities.
And that contrast reveals something useful.

Generating ideas is easy.
Creating the right environment for ideas to surface — that’s where the real work happens.

Over the past few weeks, a growing narrative suggests that AI systems could soon perform work that traditionally required entire teams of researchers, engineers, or analysts.

These discussions highlight how rapidly AI capabilities are advancing.

Modern AI systems can search vast information sources, synthesize insights, generate code, and accelerate analysis at a pace that was difficult to imagine just a few years ago.

But many of these comparisons assume that knowledge work is simply a collection of tasks that can be automated.

In reality, enterprise work operates very differently.
It is not just about producing outputs.

It is about delivering reliable outcomes within complex systems. For example:

• Generating code is not the same as building production-ready systems with security, reliability, observability, and operational resilience.
• Producing research summaries is not the same as validating insights against real organizational data, constraints, and decision frameworks.
• Creating architecture diagrams is not the same as integrating systems across complex enterprise environments.
• Generating confident answers is not the same as establishing governance, accountability, and traceability.
• Running an AI agent is not the same as operating a system that must be trusted, versioned, monitored, and governed over time.

AI will undoubtedly compress large parts of knowledge work and accelerate research dramatically.

But turning outputs into trusted outcomes requires engineering discipline, domain judgment, and responsible oversight.

Because in real systems, the hardest part is rarely generating the work.
It is owning the consequences of it.