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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 pipelines, the answer is design-time muscle memory, not architecture. A customer-facing assistant I describe in this issue was running four model-based safety checks per call — and the wrap was consistently consuming more tokens than the assistant itself.

Day 16 lays out a four-rung ladder — Scope, Tier, Cache, Escalate — for paying for risk, not for routine.

Read the full issue.

Green Software Foundation #greenai #leanagenticai #aieconomics

The future of enterprise AI is not about maximizing intelligence. It is about delivering the minimum intelligence required to achieve the maximum business outcome.

This idea has increasingly shaped how I think about the next phase of enterprise AI.

Over the past few years, we’ve witnessed remarkable advances in frontier AI models. The pace of innovation has been extraordinary, fundamentally changing what organizations believe is possible.

But as enterprises move from experimentation to production, a new set of questions is emerging:
➡️ How do we operationalize intelligence at scale?
➡️ How do we govern autonomous agents?
➡️ How do we ensure security, compliance, and data sovereignty?
➡️ How do we optimize for cost, latency, energy, and carbon?
➡️ How do we translate frontier intelligence into measurable business outcomes?

In the latest edition of the Technology Bytes newsletter, I explore why the next competitive frontier in enterprise AI is not simply more intelligence.

It’s about integrating intelligence into enterprise systems, governing it responsibly, and optimizing it for outcomes.

I also introduce the concept of Intelligence Economics — measuring AI not just by tokens consumed, but by outcomes delivered per dollar, per joule, per gram of CO₂, and per second of latency.

As frontier intelligence becomes widely accessible, organizations that succeed will be those that can operationalize it most effectively and efficiently.
This is the essence of what I call Lean Agentic AI.

📖 Read the latest edition for details

What do you think will differentiate enterprise AI leaders over the next 3 years: access to frontier models, or the ability to operationalize intelligence at scale?

Day 15 of Green, Efficient AI is live — The Route Before the Model.

The model is usually the most expensive component in an AI system. So why does every request still reach it?

In many production pipelines, the answer is reflex, not architecture. A telecom team I describe in this issue discovered that the bulk of their LLM traffic was answering questions a database could have handled in milliseconds.

Day 15 lays out a four-rung ladder — Filter, Classify, Route, Call — for the route every request should travel before it ever sees the model.

Green Software Foundation #greenai #efficentai

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 performance.

None of those choices were carbon-aware. Most of them are still in place.

The same inference call can carry very different carbon footprints depending on where and when it runs. The model does not know. The bill is roughly the same. The atmosphere is not.

Today’s issue is about the part of the stack almost nobody owns — and one of the most underused efficiency levers in AI systems today.

Green Software Foundation #greenai #efficentai #aieconomics #leanagenticai

Last week, I had the opportunity to deliver the keynote at the CleanEnviro Summit Singapore Catalyst 2026 in Singapore, hosted by the National Environment Agency.

Dr. Janil Puthucheary, Senior Minister of State, Ministry of Sustainability and the Environment & Ministry of Education, opened the event by setting the context for the day’s discussions on sustainability. He highlighted Singapore’s progress over the past decade, including reductions in waste generation, increased household recycling participation, and greater public awareness of recycling. At the same time, he underscored the challenges ahead—from rising collection and freight costs to volatile recyclables markets and the need for innovation to extend the lifespan of Semakau Landfill beyond 2035.

It was inspiring to see Singapore’s long-term commitment to sustainability come through so clearly.

My keynote, “Innovating Towards a Sustainable Future,” built on that theme and focused on three ideas:

— Sustainability and business value are not competing priorities. Increasingly, they are the same path.
— The Environmental Services industry is moving from activity-based metrics (bins emptied, routes completed) toward outcome-based metrics (waste diverted, emissions reduced, and materials kept in circular systems). That transition represents one of the most important innovation opportunities of the next decade.
— Green AI has two dimensions. Green BY technology—using AI to make Environmental Services more sustainable. And Green IN technology—ensuring the AI itself is efficient, responsible, and sustainable. Environmental Services has a unique opportunity to advance both simultaneously from the outset.

What stayed with me most was the ecosystem represented in the room: service providers, premises owners, innovators, government agencies, investors, and academics working together on a shared challenge. The Innovation Showcase brought this collaboration to life through demonstrations spanning Autonomous Systems, Analytics, AI, and Advanced Biology—showing how quickly sustainability innovation is moving from concept to deployment.

Thank you to the National Environment Agency for the invitation, to Dr Janil Puthucheary for setting the tone, and to everyone who shared ideas and perspectives throughout the event.

As Executive Director of the Green Software Foundation, I am particularly encouraged by Singapore’s leadership in sustainable technology. Singapore was the first government to join the Green Software Foundation, demonstrating how public-sector leadership can help accelerate the adoption of sustainable technology practices and standards..

Singapore continues to demonstrate how policy, industry, and innovation can come together to accelerate sustainable outcomes.

Day 13 of Green, Efficient AI is live — The Queue You Never Built.

Most inference traffic is not waiting on a human. It is waiting on a loop.
Embeddings for an index that refreshes hourly. Classifications for a moderation queue. Summaries for a morning digest. Evaluations that run overnight.
None of these need a sub-second answer. Almost all of them are dispatched as if they did.

The GPU does not care whether it processes one request or thirty in the same forward pass. The bill does. The grid does.

Today’s issue is about the latency budget nobody set, and what changes when the question is finally asked.

Green Software Foundation #greenai #efficentai

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 all of it from scratch, every time, paying the energy bill, the cost bill, and the latency bill twice for an output it already computed once.

The fix is not clever prompting. It is treating the prompt as a data structure with a stable part and a variable part, and telling the system which is which.

The four-rung ladder for fixing it, in today’s issue.

Green Software Foundation #greenai #efficentai #aieconomics #leanagenticai

If you’re wondering, like I was, why Anthropic’s latest model Fable suddenly shows as unavailable… this may be why.

Halfway through working on Fable, access suddenly disappeared.

The initial assumption?
A temporary issue. Capacity constraints. Service overload.
With the pace at which frontier AI models are being adopted, that felt like the obvious explanation.

But after digging deeper and reviewing the latest developments, it became clear this is part of a much broader shift.

Recent reports indicate that access to some of Anthropic’s latest frontier models, including Fable 5 and Mythos 5, has been impacted by new U.S. export-control measures tied to national security considerations.

Stepping back, this feels like an important moment for the AI ecosystem.

As AI capabilities advance, frontier models are increasingly being viewed not only as technology products, but also as strategic capabilities. It is understandable that governments will evaluate access through the lens of security, responsible deployment, and long-term societal impact.

For enterprises, countries, and builders, this reinforces an important lesson:
The future of AI strategy cannot rely on a single model or provider.

Diversity in model choices, open ecosystems, local capability building, and resilient AI architectures may become increasingly important in a world where access can evolve due to policy, regulation, or geopolitical considerations.

One more dimension for AI leaders to design for: access resilience.
Not just:
✔️ Model performance
✔️ Cost
✔️ Safety & Responsible AI
✔️ Latency & Reliability
✔️ Sustainability (Cost, Carbon, Water, Energy)

But also:
✔️ What happens if access to a frontier model changes overnight?

The strongest AI strategies may increasingly be the ones designed for optionality, adaptability, resilience, and responsible scale.

Day 11 of Green, Efficient AI is live — Reset the Loop When Context Decays.

An agent runs for fifteen turns. Each turn costs more than the one before it. By the last few turns, a single turn can cost more than the first several combined.
The questions did not get harder. The loop got heavier.

Most of what a long-running agent carries forward is residue — retracted reasoning, verbose tool payloads, instructions the agent has already satisfied.
None of it is being used on the next turn. All of it is being re-processed on every turn.

There is a crossover point where a fresh loop, started with a four-line summary, is cheaper than another turn of the old one. Knowing where that point is, in today’s issue.

Green Software Foundation #greenai #efficentai #leanagenticai

Day 10 of Green, Efficient AI is live — Close the Loop with the Right Tool.

Two agents, same customer question: “What’s the status of my last order, and when will it arrive?

Agent A has four clean tools — look up the customer, list orders, fetch shipping, fetch carrier ETA. Five turns to assemble the answer.

Agent B has one tool that already understands the question: latest order status for this customer. Two turns. Done.

Same model. Same data. The difference was whether the tools were shaped to the agent’s task or to your system’s capabilities.

Every tool an agent calls either ends a turn or feeds the next one. The interface, not the model, decides which.

The shape that closes a turn, in today’s issue.

Green Software Foundation #greenai #efficentai #leanagenticai

🚨 The Token Shock — Why Companies Are Running Out of AI Budget
🎙️ Episode 91 of Agentic AI: The Future of Intelligent Systems is out now.

Are we creating value… or simply generating activity?

That may quietly become one of the biggest questions organizations ask in the age of Agentic AI.

Because intelligence is no longer free to scale endlessly.

As copilots, coding assistants, and autonomous agents become part of everyday work, a new enterprise challenge is emerging:

Token Shock.

The moment organizations realize that AI does not behave like traditional software.

In this episode, I explore:
✅ Why organizations are beginning to experience unexpected AI cost escalation
✅ The hidden economics of tokens and reasoning
✅ Why “reasoning budgets” may become essential
✅ How invisible retries, tool usage, and agentic workflows quietly drive spend
✅ Why the future may be about smarter intelligence, not just more intelligence

Because perhaps the future winners in AI will not be the organizations consuming the most intelligence…
But the organizations using intelligence wisely.

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

More on Lean Agentic AI: leanagenticai.com

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 introduced the concept of Lean Principles for Agentic AI in my book Lean Agentic AI — focused on how organizations can apply intelligence in a lean, efficient, and purposeful way, balancing cost and environmental impact.

If you are building AI copilots, deploying agents, scaling enterprise AI, or navigating token costs, governance, and efficiency challenges, these principles and guidelines may be highly relevant.

📘 Book available worldwide on Amazon: https://amzn.to/3RQmXti
🌐 Learn more: https://leanagenticai.com/

Day 9 of Green, Efficient AI is live — Plan the Loop Before You Run It.

Two agents, same task: reconcile a billing dispute across three systems. Same model. Same tools. Same access.

One finishes in five turns. The other takes fourteen — and lands on the same answer nine turns later.

Nothing was broken. The slow one just started moving before it knew where it was going.

Without a plan, an agent decides each move from the last result it saw — never from the route as a whole. One cheap planning turn buys it sight of every step.
And it removes the turns at the start, where every wasted turn costs the most.
How to plan it, in today’s issue.

Green Software Foundation #greenai #efficentai

When did innovation become something we buy instead of something we think our way through?

The latest edition of Technology Bytes explores a question that feels increasingly relevant in the age of AI:

Has innovation shifted from ingenuity to compute?

For decades, innovation was shaped by constraints. Teams solved hard problems through creativity, efficiency, and thoughtful engineering. Today, especially in software and AI, innovation is increasingly measured through more compute, more tokens, more infrastructure, and more spend.

In this issue, I reflect on:

• Why earlier innovation often achieved more with less
• How abundance may be changing the way we solve problems
• The hidden environmental and economic costs of compute-first thinking
• Why efficiency and thoughtful design still matter in the AI era
• What it means to return innovation to its roots

A reflection on whether scale is enhancing innovation, or quietly becoming a substitute for it.

Encouraging to see SCI for AI recognized in the G7 French Presidency’s work on measuring and monitoring the environmental impact of AI.

What makes this recognition meaningful is that SCI for AI was shaped through deep collaboration across nearly 20 organizations spanning industry, academia, and sustainability practitioners. The specification was developed as a community effort to extend the ISO/IEC 21031 SCI standard into the AI domain, helping establish a practical and standardized way to measure AI emissions across different AI paradigms and lifecycle stages.

Recognition like this reflects the growing importance of moving from broad discussions on AI sustainability to measurable and actionable approaches that can support better infrastructure, investment, and policy decisions.

A big thank you to everyone in the Green Software Foundation community who contributed to shaping the specification.

Day 7 of Green, Efficient AI is live — Shape the Answer Before You Ask.

Most teams optimise what goes into an AI call. Few optimise what comes back.

Output tokens cost roughly three to five times what input tokens cost, across every major provider. The return trip of the call is where the meter runs fastest — and where the least design effort has been spent.

You decide what the model returns before you call it, or you pay to fix what it returns after.

How to stop paying twice for the same answer, in today’s issue.

Green Software Foundation #greenai #efficentai

Day 8 of Green, Efficient AI is live — Right-Size the Agentic Loop.

A team built an agent to triage bug reports. They expected it to cost about fourteen times a single call.

It cost several times more than that.

Nothing was broken. By turn twelve, the agent was re-sending every search, every file, and every line of its own reasoning — at full price, on every turn. Most of its tokens weren’t spent solving the bug. They were spent re-reading the loop.

A call pays once. A loop pays for everything it remembers, on every turn it takes.

How to right-size it, in today’s issue.

Green Software Foundation #greenai #efficentai

Most people know AI consumes electricity.

But very few realize that AI may also have a water story.

As Agentic AI evolves from answering questions to reasoning, planning, coordinating tools, and acting autonomously, an important question quietly emerges:

What physical resources support intelligent systems?
When AI systems run, infrastructure runs.
Servers generate heat.
Data centres cool infrastructure.
Electricity powers computation.

And in many cases, water becomes part of the story — directly through cooling systems or indirectly through power generation.

What makes this conversation even more interesting is scale.
One interaction feels small.
One extra reasoning step feels insignificant.
One retry seems harmless.

But what happens when millions of people rely on always-on intelligent systems working quietly in the background?

And perhaps the bigger question:

Does location matter?

A litre of water associated with AI infrastructure in a water-rich region is not always the same conversation as infrastructure running in areas already facing drought, groundwater depletion, or increasing scarcity.

Because the future of AI may not simply be about building smarter systems.

It may also be about building systems that are efficient, responsible, and thoughtful about the invisible resources quietly supporting intelligence.

🎙️ Episode 90 of Agentic AI — The Future of Intelligent Systems is now live: Understanding the Water Footprint of Agentic AI

Listen here:
https://lnkd.in/dvx8jnj2

Day 6 of Green, Efficient AI is live — The Cheapest Tokens Are the Ones You Never Retrieve.

A team was sending their model an 8,600-token prompt to answer a question
whose actual answer was one paragraph.

Twenty passages retrieved. One useful. Nineteen paid for in full.

The model was never the problem. The retrieval layer was — and most teams have never looked at it.

Most prompts in production are not written. They are assembled. And the cheapest tokens in any prompt are the ones you never retrieve.

How to find and fix the waste, in today’s issue.

Green Software Foundation #greenai #efficentai

Day 5 of Green, Efficient AI is live — Spend Tokens Like They Cost Money.

What if the largest pool of waste in your AI system is not the model you chose, or the calls you made, but the words you packed into every prompt?

Most production prompts are two to ten times longer than the task requires. The next principle is what to do about it.

Green Software Foundation #greenai #efficentai