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It was great to present at Green IO London 2026 on Green Agentic AI, exploring how we can embed sustainability into the design and operation of AI agents, measure their impact across carbon, energy, and water, and build more resource-efficient intelligent systems.

Our Green Software Foundation team was also present at the event, engaging with the broader digital sustainability community and exchanging ideas and perspectives.

Beyond the session, the hallway conversations made the experience even more valuable, offering opportunities to learn from others, exchange perspectives, and explore new ideas and collaborations around digital sustainability.

A big thank you to Gaël Duez, Sandra Sydow, Oliver Cronk, Meibel Dabodabo, and the entire Green IO team for organizing such an engaging event.

Next up is Green IO Paris 2026 in December. Do join the conversation and connect with the growing community advancing digital sustainability: https://lnkd.in/eDjU4DDG

100,000+ subscribers! 🎉 The YouTube Silver Creator Award arrived this week.

Over the years, sharing knowledge has been an important part of my journey. Whether through writing, speaking, teaching, or creating content, my motivation has remained simple: to explore ideas, deepen my understanding, and hopefully make it useful for others.

What started as a passion for exploring technology, AI, sustainability, and emerging innovations has gradually grown into a wonderful community.

Looking back, this journey has reinforced my belief that curiosity, consistency, and a willingness to keep learning can create something meaningful over time.

Beyond the numbers, what matters most to me is knowing that something I created may have helped someone learn, explore a new idea, or see technology from a different perspective.

A heartfelt thank you to everyone who has subscribed, watched, shared, or engaged with my content. Your encouragement and support mean a lot.

There is still so much to learn and explore.

Looking forward to continuing this journey together!

🔗 Do subscribe: https://lnkd.in/djvxYuFq

A few days in Brighton with our team at the Green Software Foundation.

Most of our time together is spent on calls, working through standards, open source projects, education, partnerships and everything else happening across the Foundation. So it was great to be in the same place for our offsite, with some of the team joining virtually as well.

We had plenty of conversations about where GSF is heading, what we want to do better, and how we can have greater impact as software and AI continue to evolve.

Some of my favourite moments, though, were outside the meeting room: walking around Brighton, time by the sea, shared meals, and conversations without an agenda.

On a distributed team, that’s where much of the trust behind our work gets built. Looking forward to taking these ideas into the months ahead.

I’m also filming parts of this trip, and experimenting with more travel and behind-the-scenes content around the conferences I’ll be presenting at over the coming weeks. The first vlog will be on my YouTube channel soon: https://lnkd.in/djvxYuFq

If you are interested in AI, technology, sustainability and some of the journey behind the work, do subscribe.

We are engineering harnesses to make AI agents more capable.
We should also engineer them to make better use of resources.

That is the idea behind Sustainable Harness Engineering.

As agentic AI evolves, the model is only one part of the system. The harness around it increasingly shapes how the agent behaves: the context it receives, the tools it can use, the constraints it follows, the actions it can take, and the feedback it receives.

So why not build sustainability into that same layer?

A sustainable harness could help agents understand the resource implications of their decisions across energy, carbon, water and waste.

The important part is not simply minimizing one metric. It is understanding the trade-offs.

A smaller model may consume fewer resources but require more retries. Caching may reduce computation while increasing storage. A lower-carbon location may have different water implications. More agents may improve reliability while increasing overall resource use.

This is where harness engineering becomes particularly interesting.

The harness can provide agents with sustainability context, resource signals and feedback, alongside the mechanisms we already design for reliability, security, performance and autonomy.

The question becomes:
What outcome are we trying to achieve, what resources are required to achieve it, and what trade-offs are we making along the way?

As harness engineering becomes a core part of agentic AI architecture, sustainability should be built into it from the beginning.

That is Sustainable Harness Engineering.

I’ll explore what this could look like in practice in my next Technology Bytes issue.

35 sponsored posts.
30 posts outside my current focus.
7 engagement-bait posts.
5 hiring posts.
4 celebration threads.

81 posts filtered in roughly 15 minutes of scrolling LinkedIn.

That number surprised me.
Not because those posts should not exist.
They just were not what I opened LinkedIn for at that moment.

LinkedIn is my go-to professional platform. It is where I learn, share ideas, discover new work and connect with some of the smartest people in my field.

And because I spend so much time here, I started thinking about a simple question:

What if my LinkedIn feed could adapt to what I want to focus on right now?

So I built myself a lens. FeedLens.

I originally built it for myself. The more I used it, the more I wondered whether others had the same problem.

The model is deliberately simple:

• I care about: topics and keywords you always want to see
• I don’t care about: topics you would rather filter out
• Focus Mode: see only what matters to you right now
• Mute noise: sponsored posts, engagement bait and other categories you choose
• AI Slop Control: an experimental on-device filter for repetitive and low-signal posts
• A counter that keeps receipts: which is exactly where these numbers came from

And one principle was non-negotiable:

Everything stays on your device.
No servers.
No account.
No analytics.
Nothing you read is sent anywhere.

The idea can be summarized in one line:
LinkedIn → FeedLens → Your signal

But the interesting part was not what FeedLens removed.
It was what became easier to notice once the noise was gone.

For me, that meant more of the conversations I actually come to LinkedIn for, and the people working on them.

And I think there is a bigger shift happening here.

AI has made content creation abundant. Attention is becoming the scarce resource.

AI is opening up incredible opportunities for more people to create, share ideas and contribute knowledge. But as the volume of content grows, the next generation of tools should not only help us create more. They should also help us decide what deserves our attention.

Imagine opening LinkedIn and being able to say:
What do I care about today?
What do I want to see more of?
What can I ignore for now?
How focused do I want my feed to be?

That kind of control could make an already valuable professional network even more useful.

I would love to eventually see capabilities like this built directly into LinkedIn. If FeedLens becomes unnecessary because the platform gives everyone this level of control, I would consider that a success.

Download FeedLens from the Chrome Web Store: https://lnkd.in/dQ2TuP2T

One question: if you could control one thing about your LinkedIn feed today, what would it be?

London, I’ll see you next week at Green IO London 2026.

As AI moves from individual prompts to autonomous agents, the sustainability equation changes significantly.

An agent does not make one model call. It can plan, reason, invoke tools, retrieve data, call other agents, retry tasks and keep working until a goal is complete.

That means a single task can have a very different cost depending on how the agentic system is designed.

At Green IO London, I’ll be speaking about:
Green Agentic AI: Optimizing Cost, Energy and Carbon

I’ll explore how we can design agentic AI systems that deliver the intelligence we need while reducing unnecessary computation, energy use, carbon emissions and financial cost.

One of the key ideas I’ll discuss:
Same task. Same agent. Very different resource consumption. That difference is architecture, not just model choice.

We all have a role to play in making AI and software more sustainable, whether we are designing architectures, building applications, operating infrastructure, developing AI systems or making technology decisions.

If you are in London and want to be part of that conversation, I would love to see you there.

📍 Green IO London 2026
📅 September 30 – October 1
📍 Convene Sancroft, St. Paul’s, London

You can register using my link: https://lnkd.in/dCh4DWXm

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 good at reasoning.

But as AI moves into agents, enterprise workflows and everyday operations, I believe we need to ask a different question:
Why should a system keep paying to rediscover what it already knows?

Humans do not reason from scratch every time.
We:
Think → Learn → Remember → Reuse → Notice change → Think again

AI systems should be able to mature in the same way.

In the book, I introduce several ideas around this:

• Learning Dividend: experience should make future work easier
• Reasoning Budget: spend inference where uncertainty actually remains
• Reuse Boundary: know not only what can be reused, but where reuse stops
• Selective Reasoning: Known → Reuse. Unknown → Reason. Uncertain → Escalate.

The bigger idea is simple:

The first era of AI scale was about making intelligence available on demand.
The next era will be about making intelligence accumulate.

Because the most advanced AI system may not be the one that reasons the most.
It may be the one that knows when not to reason.

Reusable Intelligence is now available on Amazon.

If you are building AI agents, enterprise AI platforms, or thinking about the economics of AI at scale, I hope this gives you a different lens for what comes next.

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 in compute and energy, and more reusable?
Can it recognize when a problem has already been solved and avoid paying to reason through it again?

Because the next test of intelligence may not be how much intelligence a system can use.
It may be how intelligently it decides when not to use more.

Read the latest edition of Technology Bytes:
AGI Has Arrived. Now Give It a Simpler Problem.

I’m looking forward to speaking at Green IO London 2026 on:

Green Agentic AI: Optimizing Cost, Energy and Carbon

Agentic AI can consume orders of magnitude more compute than a simple chat interaction.

As agents reason, call tools, retrieve context, retry tasks, maintain memory and coordinate workflows, efficiency becomes a design requirement, not an afterthought.

And this is not only an environmental issue.

More efficient AI also means lower operational cost, better resource utilization and more sustainable economics at scale.

In this session, I’ll explore how we can measure and systematically reduce the impact of agentic AI, including:

• Cost, energy, carbon and water per task
• SCI for AI and emerging SEI and SWI metrics
• A six-stage agent lifecycle for identifying optimization opportunities
• Design levers across models, context, tools, retries, memory and scheduling
• How GSF standards can provide a consistent measurement foundation
• A practical Monday-morning checklist teams can start applying immediately

The goal is not simply to build more capable agents.

It is to build agents that deliver more intelligence with less resource consumption and lower operational cost.

📍 Green IO London 2026
📅 September 30th – October 1st, 2026
📍 Convene Sancroft, St. Paul’s, London

Hope to see many of you there.

https://lnkd.in/dWsK9Nq4

#GreenAI #AgenticAI #GreenSoftware #SustainableAI #AI #SoftwareSustainability #SCI Green Software Foundation Green IO 🎙️ Gaël DUEZ

100 episodes. One evolving question: what does the future of intelligent systems really look like?

When I started Agentic AI: The Future of Intelligent Systems, the conversation around AI was very different.

We were focused on models, prompts and copilots.
Then came agents.

Now the discussion is moving again, toward something much bigger:
enterprise intelligence.

That is why Episode 100 is titled:
The Next Era of AI: From Agents to Enterprise Intelligence

And if you have been wondering when we will achieve AGI, I think there is another question worth asking first.
AGI may still be a long way ahead.

Before we get there, most enterprises have not yet fully learned how to use the intelligence we already have.

We are still figuring out how to connect agents across the enterprise.

• How to give them the right context.
• How to reuse intelligence instead of reasoning from scratch every time.
• How to govern memory, tools, data and decisions.
• How to move from impressive demonstrations to reliable business execution.

So perhaps the next major milestone is not AGI.
It is enterprise intelligence.

Because the next chapter will not be defined by how many agents an enterprise can deploy.

It will be defined by how effectively intelligence can be shared, reused, governed and connected to real business outcomes.

Reaching 100 episodes is a very special milestone for me.

A huge thank you to everyone who has listened, followed, shared an episode, sent feedback, challenged an idea, or simply spent a few minutes thinking about the future of AI with me.

Every listen matters. Every conversation adds something.
The first 100 episodes explored the rise of Agentic AI.
The next 100 will explore what comes after.

From models → agents → enterprise intelligence.
🎙️ You can find the Spotify podcast link below.

If you have been enjoying the conversations, please follow the podcast, share an episode with someone interested in the future of AI, and help more people discover the series.

Thank you for being part of the journey.
The next chapter starts now.

AI leadership is not about chasing the next model. It is about building enterprise intelligence that survives the next model.

A new AI model is released. It is smarter, faster and cheaper. Does your strategy change?

AGI is “near.” Does your strategy change again?

There is a vast difference between using a model and building enterprise capability on top of it.

A new model may improve reasoning, coding, multimodality or cost. But it will not suddenly understand your business, redesign your processes, integrate with your systems, create your governance, or build differentiated intelligence for your enterprise.

That intelligence has to be engineered.

As I wrote in an earlier Technology Bytes article, this is also the difference between an AI-aware leader and an AI-fluent leader.

An AI-aware leader follows what models can do.
An AI-fluent leader understands what those capabilities mean for the business, what needs
to be built around them, and what should remain stable even as the underlying models change.

So after every major model release, the question should not be:

Do we need a new AI strategy?

It should be:

Does this model materially improve a business capability we have already decided matters?

Models will keep changing.

Enterprise intelligence is the asset. And to make that intelligence durable, enterprises will need Reusable Intelligence.

I will explore that idea in an upcoming Technology Bytes.

The internet taught companies to design information for humans.
AI may force companies to redesign information for machines.

For decades, the content pipeline was simple.
A company created information.

A human found it, read it, interpreted it and acted on it.

That shaped everything:
Websites.
Reports.
Dashboards.
Documentation.
Product pages.
Knowledge bases.

But the pipeline is changing.

AI is now used to:
Ideate → Design → Code → Develop → Test → Deploy → Document

Then the product reaches the customer.
And increasingly, the customer does not consume all of that information directly.
Their AI agent does.

The agent:
Searches → Reads → Summarizes → Compares → Recommends → Plans → Acts

Which creates a strange new loop:
AI creates the information.
Another AI consumes the information.
Yet the artifact in the middle is still designed for a human.

AI writes a 30-page report so another AI can summarize it.
AI creates documentation so another agent can extract the relevant steps.
AI builds dashboards so another AI can interpret the underlying metrics.
AI generates product pages so a customer agent can compare the products.

At some point, we should ask:
Why are we still generating the intermediate artifact at all?

The next content architecture may look very different:
Intent → Structured Intelligence → Agent → Action → Outcome → Learning

The unit of information may no longer be the page, report or dashboard.

It may be a machine-consumable intelligence object containing:
Facts.
Claims.
Evidence.
Constraints.
Capabilities.
Actions.
Dependencies.
Freshness.
Something an agent can directly discover, reason over and act upon.

This changes more than content creation.
A website could become an agent interface.
A report could become a decision object.
Documentation could become executable knowledge.
A dashboard could become an intervention layer, surfaced only when something requires attention.

And SEO may eventually be joined by something very different:
Agent Experience Optimization.

Not simply:
Can people find our information?
But:
Can an AI agent correctly discover, understand, trust and act on our intelligence?

We spent the last 30 years building the internet for human attention.
We may spend the next decade rebuilding enterprise information for machine consumption.

Is your leadership team the biggest risk to your AI strategy?

Most organizations are investing in AI.
But if leadership understanding is not evolving as fast as the technology, strategy starts falling behind.

That is the Leadership Adoption Gap.

In the latest Technology Bytes:
The Leadership Adoption Gap: Why AI Strategies Fail at the Top

Are your leaders AI-aware, or genuinely AI-fluent?

What if AI, and the data center behind it, had a voice?

What would it say about the jobs we fear losing?
The energy and water it consumes?
The infrastructure we keep building?

And our relentless demand for more intelligence, more speed, and more compute?

I imagined a conversation between a human and a robot.
🎥 Watch till the end.

#AI #ArtificialIntelligence #SustainableAI #ResponsibleAI #DataCenters Green Software Foundation

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 business processes, systems, controls and economics.

That is the AI Delivery Gap.

In the latest edition of Technology Bytes, I explore why better models do not automatically create better AI systems, and why the next competitive advantage may come from how well enterprises turn intelligence into delivery.

Research asks whether AI can do it. Delivery asks whether the enterprise can depend on it.

Read: The AI Delivery Gap: Why Better Models Do Not Automatically Create Better AI Systems

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 keep paying to reason through the same logic again?

That is the idea behind my latest Technology Bytes:
The Enterprise Intelligence Compiler: From Probabilistic Reasoning to Deterministic Scale

Use AI where intelligence is actually needed: for the novel, unknown and exception paths.

Then take validated reasoning and turn it into executable business artifacts such as rules, workflows, policies, code, decision tables and controls.

That changes the economics:
Variable inference cost → Predictable execution cost
Probabilistic reasoning → Deterministic execution
Repeated inference → Reusable intelligence
Model behavior → Governed business logic
Scaling tokens → Scaling transactions

The goal is not to remove AI.
It is to stop using AI for problems the enterprise has already solved.

Reason until you know. Then execute what you know at scale.
If AI is going to scale across the enterprise, we need to move beyond an economics model where value is tied to how many tokens we consume.

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 and validated, much of it becomes repetitive: approve, validate, route, calculate, reconcile, provision, enforce policy. The inputs may change, but the underlying logic often does not.

Still, we put an LLM in the runtime path and ask it to reason through the same class of decisions again and again.

And every time it thinks, the meter runs.
More tokens. More inference. More latency. More variability. More cost.

This is where token economics starts to break down at enterprise scale.

Tokens measure what AI consumed. They do not measure what the business accomplished.

Caching helps, but caching only makes repeated inference cheaper.
Codification means AI does not need to solve the same known problem again.

A better model is:
Discover with AI → Validate → Codify → Execute deterministically → Escalate exceptions/new situations to AI → Learn → Codify again

Use AI when something is unknown, ambiguous or genuinely new. Once that reasoning is validated, turn it into an executable artifact: a rule, workflow, API, code, state machine, or a compact capability that can run like a library on any system.

Then execute it repeatedly without invoking an LLM every time.

When a genuinely new situation appears, escalate it back to AI, solve it, validate the outcome, and codify what was learned.

Over time, the deterministic surface of the enterprise grows, while AI focuses on what is actually new.

This changes the economics fundamentally.

Instead of paying for intelligence every time knowledge is used, pay for intelligence when intelligence is actually required.

If AI is going to scale across enterprises, we need to move beyond an architecture where tokens become the unit of business execution.

The goal is not to minimize tokens.
The goal is to maximize business value while minimizing the intelligence required at runtime.

Reason where necessary. Codify what is learned. Execute what is known. Escalate what is new.

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 again.
That makes sense when the problem is genuinely new.

But once the organization has learned the answer, why should the reasoning remain in the execution path?

The term I see emerging for this next stage is Codified AI.

Codified AI converts validated AI reasoning into deterministic, executable artifacts that can be reused without invoking an LLM again.

The progression looks like this:
Novel → Reason → Learn → Bound → Codify → Execute

When something is unknown, use the best intelligence available to discover the answer.
As the problem becomes understood, reduce the intelligence required.
And when the logic becomes explicit and independently testable, convert it into workflows, rules, policies, decision tables, code and tests.
Then run it deterministically.

Zero LLM calls in the validated execution path.
This is the broader idea behind the Intelligence Curve:

Use maximum intelligence to discover. Use minimum intelligence to execute.

The shift is bigger than token savings.

It is a move from consuming intelligence repeatedly to converting intelligence into reusable organizational capital.

One reasoning session can become a workflow executed millions of times.
One policy analysis can become an approval engine.
One design session can become an operational control.

And even if inference becomes nearly free, determinism does not arrive with the discount.
Reproducibility, auditability, bounded behavior, predictable execution and governance still matter.

Perhaps AI maturity should therefore not be measured by how many agents, model calls or tokens an organization consumes.

It should be measured by how effectively the organization converts what AI learns into reusable execution.

I explore this in my new white paper:
The Intelligence Curve: From Frontier Reasoning to Codified Execution

White paper attached.

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 intelligence?

Today, much of AI economics is measured at the point of consumption: tokens, API calls, GPU hours, model costs and infrastructure spend.

These measures tell us what AI consumed.

They don’t tell us what AI accomplished.

That is the starting point for AI Economics.

I believe the economic unit of AI needs to move closer to the reason we deploy AI:

Compute → Output → Tasks → Successful Tasks → Outcomes → Value

And as we move from models and copilots toward agents and reusable skills, differentiation moves upward:

Model → Capability → Skill → Domain → Industry → Enterprise

The ability to encode domain knowledge, industry expertise and enterprise context into reusable, executable intelligence could become an important source of competitive advantage.

But AI economics cannot stop at financial cost.

AI has a physical economy. Intelligence ultimately depends on infrastructure, hardware, energy, water and materials.

We therefore need holistic AI unit economics spanning finance, infrastructure and physical sustainability.

Not simply what AI costs, but what outcome it creates, what value that outcome delivers, and what resources were required to deliver it.

This leads us toward measures such as cost per outcome, energy per outcome, carbon per outcome and value per unit of resource, alongside absolute consumption.

There is also one industry where I believe this shift will be particularly profound: IT services.

For decades, much of the industry has scaled through:

People × Utilisation × Rate = Revenue

But what happens when expertise becomes executable?

When agents perform work and skills encode reusable expertise, the delivery model starts moving toward:

People + Agents + Skills + Platforms + Domain Expertise → Outcomes

This has implications for billable hours, offshore delivery, talent pyramids, managed services, pricing, margins, intellectual property and outcome-based contracts.

I dedicate a part of the book to this transition and what an AI-native services firm could look like.

AI isn’t simply changing how IT services firms deliver work. It could change what they sell, how they price it, how they scale expertise, and where they capture value.

AI Economics brings these ideas together by connecting intelligence, skills, outcomes, resources and value into one economic system.

As intelligence becomes increasingly abundant, the question becomes:

What should we use intelligence for, and what value can we create with it?

📘 AI Economics: Building and Scaling Intelligence is now available in paperback on Amazon worldwide. Link in the comments section.

Do you think AI is actually reducing work?

It is certainly reducing the cost of producing work. Analysis that took days can now be generated in minutes. Code appears in seconds. Ten scenarios can be created where previously there might have been two.

But something interesting happens next.

Organizations rarely take that productivity gain and simply stop. They ask for more analysis, more variants, more scenarios, more recommendations, more output.

The bottleneck does not disappear. It moves.

Before AI, scarcity often sat with the people producing the work. Increasingly, it sits with the people who must review it, prioritize it, make decisions, and act on it.

That is the AI Work Paradox.

AI can make every producer more productive while simultaneously increasing the amount of work flowing toward managers, reviewers, architects, editors, and decision-makers.

Which leads to a very different AI productivity question:
Not: How much more can we produce with AI?
But: How much additional intelligence can the organization actually absorb and convert into value?

In the latest edition of Technology Bytes, I explore why AI may be shifting the real constraint from production capacity to absorption capacity, why “hours saved” tells only half the story, and where those saved hours actually go.

The AI Work Paradox: The Real Constraint Is Absorption