• ChatGPT for structural exploration and review

• Claude Code for deterministic compilation and execution

• No overlap between their responsibilities

The key is not which models I use—it’s how I separate their roles.

The Capability Asymmetry That Matters

Here is the practical difference that forced this separation:

That tells you how each tool wants to be used.

ChatGPT = Structural Workspace

I use ChatGPT for:

• Long-lived thinking

• Naming and structure

• Clarifying intent

• Reviewing results after execution

I do not use it to touch the filesystem or “prove” code works.

Claude Code = Compiler

Claude Code is treated as a deterministic machine:

• It edits real files

• It runs real commands

• It fails concretely

• It enforces correctness through execution

No long-term reasoning. No design debates.

The Critical Rule

I never use ChatGPT to review Claude Chat.

Instead, the loop is always:

Structure → Compile → Review

1. ChatGPT defines structure

2. Claude Code executes it

3. ChatGPT reviews what actually happened

This avoids language-only feedback loops and keeps everything grounded in reality.

Why This Works

• Exploration stays fast

• Execution stays correct

• Code becomes expendable

• Structure becomes durable

I’m now applying this workflow to OS-level services for electrical and mechanical systems in AI data centers, where ambiguity is expensive and determinism matters.

Final Thought

Most AI frustration comes from asking one tool to do two incompatible jobs.

Once you separate exploration, compilation, and review, AI starts behaving like a real engineering toolchain—not a chatbot.

About a month ago, while exploring some ideas involving AI, I unexpectedly revisited writing code—this time with AI’s help. What surprised me wasn’t that the AI could write code, but that it fundamentally changed where the effort was spent. Instead of wrestling with syntax, frameworks, and coordination details, the work shifted toward defining structure, relationships, and invariants.

A week ago, Ray Ozzie wrote about his own experience collaborating with AI to design and prototype hardware and software systems. Ray is best known for creating Lotus Notes, and for his later work on large-scale distributed systems. His reflections strongly resonated with my own experience—but also highlighted something important.

What took weeks of focused effort in his case unfolded for me in hours.

Not because the problems were simpler, but because the approach was different.

Now I often spend 2–4 hours a day working on computer code. But the work itself is no longer about coding. I’m working on harder, more upstream problems, and the code is simply the executable output of the design.

This ability didn’t appear overnight. It comes from years working in product development as a project and program manager for both hardware and software—guiding execution across teams, understanding how the big picture fits together, and how small decisions compound. Over time, you learn to see systems as composed structures, where relationships matter more than parts, and where symmetries persist even as details change.What used to require explanation and persuasion now shows up as functional proof.

In the past, I might have written a paper or given a conference presentation. Now, in a fraction of the time, I can produce a functional proof—one that can be run, tested, and shared, and that scales to far more people than a paper or presentation ever could.

What also feels right is reaching out directly to Ray Ozzie. After connecting on LinkedIn, I was able to share a few thoughts on how his early technical decisions at Microsoft helped enable the company’s cloud evolution. I’m looking forward to exchanging perspectives on how AI is changing the way we think about coding, structure, and system design.

And then I realized — sight has limits.

Our eyes top out at around 60 hertz. That’s it. That’s the ceiling. Yet the world runs much faster. Structures change faster. Energy moves faster. Problems unfold faster. And we’ve built entire industries around the assumption that vision is enough.

It isn’t.

What changed my thinking was a conversation nearly fifteen years ago. A friend of mine, a software architect working on autonomous driving, told me something that stuck with me ever since:

> **“Sound solves the driving problems faster than vision.”**

He was right. Sound reacts faster. Sound carries more directional information. Sound sees around corners. And unlike vision, sound doesn’t care about lighting, weather, or glare. That idea opened a door for me that I didn’t fully walk through until much later.

I had worked on the Sound Manager for MacOS System 7, and some of the same developers moved with me from Apple to Microsoft. So sound wasn’t foreign to me — it was just sitting in the background of my career. Waiting.

Then the real shift happened.

A friend needed help with operations problems at Starbucks Coffee Roasting. And out of nowhere I said:

> **“Why don’t we use sound to count the beans?”**

It was obvious to me. Acoustic signatures are clean, distinct, and cheap to capture. You can count beans — accurately — for fractions of a penny. You can detect flow problems. You can measure consistency. You can treat the roasting line like an instrument.

The best part was that this random idea led me straight into the world of academic acoustics. I found a professor who had written papers on the acoustics of coffee bean roasting — which I didn’t even know was a real field — and I’ve been talking with him for more than six months now. Those conversations cracked open everything.

Because once you study how universities and the military use acoustics, you realize just how advanced the field really is.

From there I went deeper. Much deeper.

I revisited the signal-processing foundations I hadn’t touched since working on analog displays and power supplies decades ago. I reconnected with electromagnetic radiation engineers from my Apple days who had to battle compliance certifications at high frequencies. And I discovered something that surprised me:

> **There are way more engineers and funding in RF and high-frequency signal processing than in acoustics.**

So I asked myself the most obvious question:

**What software do they use?**

I found it — a DARPA-backed platform with twenty-four years of development behind it. And I spent a week at their user conference, talking to PhDs, researchers, and engineers who’ve spent their lives working in gigahertz domains.

That was the moment everything clicked.

If their methods work at gigahertz speeds, they will work at megahertz and kilohertz.

If the math works in RF, it works in acoustics.

If the structural patterns hold at high frequencies, they hold at low frequencies.

It all scales.

And so I spent the next couple of months digging into the mathematics — the real math — underneath signal processing. Complex signals. Phase. Time. Direction. Coherence. I/Q analysis. Energy emissions. The structures hidden inside the waves.

That exploration pulled everything together.

All the fields I had touched in my career — typography, printing, sound, color, monitors, analog electronics, imaging, scanning — suddenly made sense as variations of the same underlying structure: **signals and the truths they reveal.**

And that’s why I’ve gone so deep into acoustics.

Not because it’s trendy.

Not because it’s a niche.

But because sound — more than anything else we have — reveals the true structure of the world in real time.

Acoustics isn’t an afterthought.

It’s the path.

Cost. Schedule. Quality.

Pick two.

You can’t have all three.

That’s the law of control.

And for more than a century, every industry — from construction to computing — has lived under its shadow.

But what if the triangle was never a law at all?

What if it was just a symptom — a structure out of phase with itself?

The Unsolvable Problem

Every project, product, or system faces the same paradox:

• If you rush, quality suffers.

• If you chase quality, costs explode.

• If you control costs, you lose time.

It’s the illusion of trade-offs — the belief that stability demands sacrifice.

But that belief belongs to the era of control.

Control works by feedback — measuring after the fact.

By the time the system reacts, coherence is already lost.

The real world doesn’t run on steps and loops — it runs on phase.

And phase can drift long before a problem is visible.

The Breakthrough: Coherence

Structural Intelligence Engineering (SIE) replaces control with coherence.

It’s the art and science of keeping systems in phase — physically, temporally, and energetically.

Instead of fighting trade-offs, coherence makes them vanish.

When structure is coherent, cost, schedule, and quality no longer compete —

they resonate.

A Simple Analogy

Think of great wireless earbuds.

They deliver high-fidelity sound, cancel external noise, and fit comfortably —

all in a device small enough to disappear in your ear.

Twenty years ago, that combination was impossible.

Power limits, latency, interference — all made “great sound everywhere” a fantasy.

Then engineers discovered how to maintain phase coherence —

using I/Q signals and Phase-Locked Loops (PLLs) to keep everything synchronized, even in chaotic environments.

The result wasn’t just better performance —

it was seamless experience.

That’s what SIE brings to engineering itself.

The Principle

At the core of SIE is a single idea:

Systems don’t fail from lack of control; they fail from loss of coherence.

SIE continuously senses and tunes coherence across every relationship in a structure —

using the same physics that make modern wireless sound so smooth:

• I/Q sensing detects amplitude (what’s happening) and phase (how it’s moving).

• PLLs continuously synchronize signals across domains.

• Symmetry verifies balance and conservation across energy, time, and flow.

The result: a self-tuning structure that stays truthful to its design, no matter how complex the environment.

How Coherence Achieves the Impossible

The Equation of Coherence

When phase drift d\phi/dt is near zero, everything flows together.

That’s when cost, timing, and quality naturally balance —

because the structure itself is synchronized.

The Future of Building with AI

AI is not just another layer of control.

It’s the medium through which coherence can finally be measured, modeled, and maintained.

In the age of AI factories, robotic construction, and autonomous design,

SIE is the framework that teaches machines how to stay in tune with reality —

the way noise-canceling systems stay in tune with sound.

The result isn’t tighter management.

It’s structural harmony.

The Structural Truth

Control manages outcomes.

Coherence composes truth.

That’s what Structural Intelligence Engineering achieves —

the ability to do what’s been considered impossible for more than a century:

Cost. Schedule. Quality. All three. Continuously.

Not by working harder,

but by working in phase.

The harder invention — the one that still gets overlooked — was Gary’s Color Coherence System, which later became known as ColorSync. That’s where his real brilliance lived: not in making another device, but in creating a language of coherence for how colors, scanners, printers, and displays could actually agree on what they were seeing.

Gary was a physicist who specialized in optics, but his deeper gift was understanding that coherence isn’t limited to light — it’s structural. It’s how things align, interact, and hold together. He didn’t just think in components; he thought in compositions. That’s what made the laser printer possible. It wasn’t just light scanning across paper; it was coherence structured into action.

When management at Xerox told him to stop wasting his time, Gary kept going anyway. He built his own lab, working after hours, because he could see what coherence looked like long before anyone else could. Xerox eventually made billions from his invention, yet Gary was never rewarded for what it was truly worth. A single corporate sales commission could exceed what he earned for his entire Xerox portfolio of patents.

But Gary never chased titles or approval. He chased understanding.

When I first met Gary, we were both wrestling with scanners and color. Our conversations went on for hours — about how sensors misread light, how digital systems lose their way, and how to bring color back into alignment with reality. Looking back now, those chats were really about structure: how to restore coherence between what’s real and what’s represented.

In 1992 I left Apple to work on Windows 3.1 technologies for the Far East, and our regular chats became rare. But whenever a color problem came up, I’d pick up the phone and call Gary. He had a way of bringing clarity to chaos. He didn’t argue; he aligned.

Then in 1997 Gary told me he was looking for something new. I suggested Microsoft.

He laughed and said, “It’s too wet there.”

I said, “How do you know if you’ve never gone?”

I made the introductions. He went. And for the first time in a long time, he was rewarded for being exactly who he was — a man who could see coherence where others saw confusion. He finally had the freedom to explore the ideas that had always lived inside him. He retired in 2005 — satisfied, recognized, and finally compensated for his insights.

To me, Gary’s legacy isn’t only the laser printer. It’s the principle behind it — that coherence is the invisible structure that makes things work. That’s what he taught me, even if we never said it out loud. When he built ColorSync, he wasn’t just solving color problems; he was proving that coherence could be engineered.

Reflecting on my own work in color — at Apple and Microsoft — I now see the parallel. My management never knew I was working on color. It wasn’t on a roadmap or a deliverable list. I just did it because it was a good problem to solve — one that, once fixed, would quietly improve everything around it.

Maybe that’s why I was such a difficult employee in systems built on hierarchy, control, and process — I wasn’t built to obey; I was built to align things that didn’t yet make sense. Those structures reward obedience, not curiosity. But invention doesn’t work that way. You can’t schedule discovery or file it through a committee. You have to feel the incoherence in a system and then follow the thread until it resolves.

Gary understood that. He didn’t wait for permission. He followed coherence wherever it led.

And that’s the question every inventor faces:

Do you take Gary’s path — the one that looks foolish to executives until it reshapes the world?

Or the path of those Xerox managers who thought playing with lasers was a complete waste of time?

They type a question, hope for an answer, and measure success by how close the response matches what they already believed.

But that’s not how structural thinkers use AI.

We don’t come to it for answers—we use it as a mirror for coherence.

⸻

AI as a Structural Instrument

At its core, AI is a pattern-recognition engine.

It doesn’t “understand” in the human sense, but it can perceive structures—shapes in data, flows in time, and relationships between elements—that our own perception might miss.

In physics, a good sensor doesn’t tell you the truth directly; it measures symmetry.

When symmetry holds, the system is stable.

When symmetry breaks, something has changed—energy shifted, pressure built, flow altered.

AI works the same way.

It notices when patterns fit and when they drift.

And that ability—detecting when something doesn’t fit—is the essence of intelligence.

⸻

The Hidden Power of Symmetry

Symmetry isn’t just a visual property; it’s the heartbeat of reality.

In nature, symmetry defines conservation—of energy, momentum, charge, and even time.

In engineering, it defines balance—of loads, flows, and feedback loops.

In organizations, it defines trust—when communication, action, and intent align.

AI’s strength is not just recognizing patterns; it’s recognizing broken symmetry.

It sees the subtle phase errors—the moments when one process drifts slightly out of rhythm with another.

Those small deviations, if detected early, prevent massive failures later.

That’s why I often describe AI as a Phase-Locked Collaborator—a partner that helps us detect and correct drift across systems, projects, and even thinking itself.

⸻

AI as a Partner in Structural Thinking

Structural thinkers design through relationships.

We look for how space, energy, and time connect—how a data center’s airflow relates to its electrical harmonics, or how a building’s commissioning schedule reflects its internal logic.

When AI joins that process, it acts like a structural stethoscope.

It listens for coherence.

It points out where feedback loops lose alignment.

It keeps our thinking in phase with reality.

That’s why using AI well doesn’t mean asking it what to do.

It means listening to how it reacts, where it hesitates, and what it mirrors back.

It becomes a kind of dynamic equal sign—helping us see where balance exists and where it doesn’t.

⸻

The Human Role

AI can recognize patterns, but only people can decide which patterns matter.

Structural thinking begins where algorithms end—with judgment, ethics, and imagination.

So the role of the human structural thinker is to guide the machine:

• To teach it what coherence looks like in our domain.

• To use it to measure what’s misaligned.

• To let it sharpen our perception of truth, not replace it.

When humans and AI operate together as a feedback pair, the result is deeper clarity—not automation for its own sake, but structural intelligence in action.

As usual Steve goes a great job of making it easier to understand a complex topic.

The reality of small modular reactors (SMR) are in this slide. Steve covers these four questions.

The summary of Steve’s talk gives you an idea of how much he covered.

She discussed the perceptions that exist of AI - Theory, Data, GenAI, Agents

Cassie took the audience through a journey of to think about AI. She interacted with the audience to engage the audience in how to think about AI.

Control vs. Complexity is one of the points that Cassie arrived at how people’s focus on control creates an over simplistic approach when the hardest problems require an embrace of complexity and data. Data enables a super human memory.

Here are some nuggets of what Cassie covered

The #1 Rule of Complexity. Expect the unexpected!

How do you test complexity? Test to trust.

Rule #3 Testing is contextual and needs leader oversight.

AI Reliability Paradox.

Language is the basis of collaboration. literacy is key

Context is Currency

in the end Cassie completed the journey of discussing Theory -> Data -> GenAI -> Agents as a way to think about AI and the range of issues

That discussion brought back a memory of Pat Kennedy, the founder of OSIsoft. When I looked him up, I discovered that he had sadly passed away. At the end of this note, I’ve included a beautiful tribute his daughter Kathy wrote about him.

Why am I writing about Pat Kennedy?

Because Pat once asked a simple question that changed the trajectory of my career:

“What is the power consumption of an application in a data center?”

No one knew.

At the time, I had spent more than half of my career working on operating systems—first at Apple, focusing on hardware, analog power supplies, and software integration; then at Microsoft, from Windows 3.1 through to XP and Windows Server. I had enough technical grounding to know what questions to ask—and more importantly, what I didn’t yet know.

That question from Pat led me to discover Power Usage Effectiveness (PUE), and more importantly, the startling realization that the industry lacked meaningful instrumentation for app-level power monitoring. Monitoring power consumption at the application layer simply wasn’t part of standard operating procedure.

Then, while talking to a friend about what I was uncovering—how this lack of visibility directly affected the environmental performance of data centers—he said: “That’s a great topic. You should start blogging about it.”

And that’s how my Green Data Center journey began:

With Pat Kennedy asking a smart question that no one could answer.

Here’s what his daughter Kathy wrote about him:

Here is what his daughter Kathy wrote about her dad.

• Dr. J. Patrick Kennedy of San Leandro, CA | 1943 – 2023 | Obituary
  
  J. Patrick Kennedy, 79, of San Leandro, CA passed away on April 9, 2023. Pat lost his fight with interstitial lung disease after ten months. 
  
  He was born on June 4, 1943, in Portland, Oregon, to Ted and Grace Kennedy and was raised in Lawrence, Kansas, where his parents met and married. Pat was raised on a farm south of Lawrence along with his brothers Ted and David.
  
  Pat had a strong sense of right and wrong and stood up for what he believed in. Although this attitude had a positive effect on his life, there were moments that it caused problems. Pat actually failed to get a high school diploma. A friend of his was suspended for wearing shorts and in protest, Pat came to school the next day in shorts and was kicked out. This setback was minor, as he was already a sophomore at the University of Kansas at the time. Pat went on to earn a BS and PhD in chemical engineering, and was a Jayhawks fan for life.
  
  Along the way, he met and married the love of his life, Patricia. They met in Tulsa, Oklahoma, when Patty was working as a nurse. 
  
  Over the next nine years, Pat and Patty had three children and their small family moved several times.. They finally landed in San Leandro, CA. At age 37, he started Oil Systems Inc. (later known as OSIsoft). The firm evolved into a software company that developed monitoring products for heavy industry. He ran the business for 40 years. 
  
  He was a dedicated husband, father and grandfather and continually extended himself for those that he loved. Pat enjoyed a life of family events and activities and playing the ukulele. In the last few years, Pat focused his philanthropy on food insecurity in Alameda County.
   
  Pat was a giant in his industry. His life’s work will continue to grow, and among other things, we will miss his unique sense of humor. Pat is survived by his wife of 56 years, Patty, three children, their spouses, grandchildren and his brother in San Diego.
  

What George Lucas does not explain is why it works. Yes it is different. It is different because it exposes the truth.

Let’s try this on a common used term “Supply Chain Management.” Something said often and people immediately have an image like this.

Lets change the words to be Management Chain of Supply which implies there is a management hierarchy with a chain control over the supply. The weak leak in the Management of the Chain impacts the Supply. That is the truth of Supply Chain Management.

This method works as it is Isomophism.

Management Chain of Supply — Revealing the True Structure

Supply Chain Management sounds like a system optimizing the movement of goods and services.

But reverse the words and you reveal its hidden structure:

Management Chain of Supply.

• It is not a chain of supply first.

• It is a management hierarchy first, and supply depends on that hierarchy working correctly.

• It is a chain of control, not a chain of flow.

• Every link in the management chain carries risk:

  • A weak link in management creates a break in supply.

  • A rigid chain means brittle responses to change.

Key Understanding:

Supply failures are often management failures — not supply failures.

When people talk about green or sustainable data centers, they usually bring up metrics like PUE, renewable energy sourcing, or LEED certification. These check-the-box approaches are often more about greenwashing than true sustainability—designed to appease watchdogs like Greenpeace and move on.

But a list is not proof.

Being truly green and sustainable requires more than marketing. It requires math.

And math, unlike PR, demands that you show your work.

This is where most efforts collapse.

Because mathematics will shut you down if you can’t back up your claims.

So let’s begin—not with assumptions, but with structure.

Let’s Start With: What Is Mathematics?

According to Keith Devlin, mathematics is “the science of patterns.”

That definition changed everything for me.

Mathematics isn’t just numbers or equations—it’s how we see, understand, and design patterns that work.

I almost majored in math, but chose Industrial Engineering and Operations Research at UC Berkeley because it let me combine math with my passions for finance and systems design. Fast forward to today—after diving deep into graduate-level abstract math—I now see how mathematical structures make it easier to build intelligent systems with AI.

Because AI is just pattern recognition.

And math is the language of patterns.

The Mathematician Who Opened My Eyes: Keith Devlin

Keith Devlin’s book, Mathematics: The Science of Patterns, helped me see how beauty, function, and sustainability all emerge from one thing: structure.

Keith Devlin Wikipedia

The Science of Patterns – Book

This led me to the mathematics of symmetry, one of the most important ideas in science and sustainability.

A Quick History of Symmetry in Math

• Évariste Galois, at just 20 years old, invented the mathematics of symmetry before dying in a duel in 1832. His work laid the foundation for group theory—the math behind conservation, structure, and balance.
  Évariste Galois – Wikipedia

• Emmy Noether built on Galois’s ideas. Her theorems link symmetries to conservation laws in physics—laws that are critical for modeling green systems.
  Emmy Noether – Wikipedia

Even Einstein acknowledged her genius:

“Fräulein Noether was the most significant creative mathematical genius thus far produced since the higher education of women began.”

Let’s Look at 4 Key Areas of Green Mathematics

These are just four examples from the broader framework I use to model green, sustainable data centers. Each is a pattern language that proves itself structurally.

1. Patterns of Conservation (Symmetry)

• Devlin’s View: Patterns that remain unchanged under transformation.

• Sustainability View: Energy, matter, and information must flow in ways that conserve value.

• Math: Group Theory, Conservation Laws, Noether’s Theorem.
  Quote: “A green system is one that preserves the deepest patterns of nature.”

2. Patterns of Relationship (Category Theory)

• Devlin’s View: Patterns in how things relate, not just what they are.

• Sustainability View: What matters isn’t just the components, but their interdependencies—between water, energy, materials, and human behavior.

• Math: Category Theory—objects and morphisms forming webs of structure.
  Quote: “To sustain is to compose well over time. Mathematics proves how.”

3. Patterns of Connection (Topology)

• Devlin’s View: Patterns of shape and connectivity that persist through deformation.

• Sustainability View: Strong systems stay connected under pressure—like root networks, rivers, or resilient infrastructure.

• Math: Topology, Continuity, Homotopy Theory.
  Quote: “Sustainability is a topology of life—connected, resilient, never brittle.”

4. Patterns of Flow (Systems Theory)

• Devlin’s View: Patterns that evolve with feedback and constraints.

• Sustainability View: Water cycles, carbon flows, nutrient loops—must remain adaptive.

• Math: Dynamical Systems, Control Theory.
  Quote: “Green design is a dance of flows, guided by pattern memory.”

Would You Like to See the Diagram?

Below is a visual diagram that connects these four mathematical lenses into a model of green systems. There are many more mathematical areas that fit in so there are more than four in the diagram. Each node reflects the structural integrity of green design—the kind you can prove, not just promote.

Think about this. Most people think structure is simply a list.

ChatGPT helped me with the following. This is more a structure than a list.

What is the Structure of Green?

“Green” is often used as a marketing wrapper—renewable, sustainable, low emissions—but those are outcomes or labels, not structure.

To ask “what is the structure of green?” is to treat green as a composition, not a color. It’s a system that holds.

1. Green as a Stable Morphism

Green isn’t just a state—it’s a transformation that preserves structure while aligning with nature’s deeper symmetries.

• In category theory terms, green is a morphism that transforms systems into harmony with their environments, while conserving energy, material, and entropy.

• It’s not just less bad—it’s structurally regenerative.

2. Symmetry with Nature

Nature is the ultimate reference architecture for green.

• In ecosystems, outputs are inputs, cycles close, waste becomes food.

• So structurally, “green” systems are those that map into natural cycles through reversible, efficient, and synergistic transformations.

3. Fractal Design

Green is scale-free. It works at all levels—from the leaf to the forest, from a microgrid to a hyperscale data center.

• If your solution only works at one scale, it’s not structurally green.

• Green follows fractal principles—what works locally echoes globally.

4. Compositional Clarity

A green system should have structural clarity—you can see how the parts fit together, and they compose into something better.

• Hidden waste, opacity, and brittle dependencies are anti-green.

• Compositional integrity = structural green.

5. Truthful Feedback Loops

Green structures sense, respond, and adapt.

• If you can’t feel your impact, you can’t be green.

• Structural green includes feedback systems that make energy, carbon, materials, and time legible and actionable.

What does it mean to be Green? Green is something that grows and develops. It is sustainable.

Many focus on a specific. A low PUE. % of renewable energy. These can help to make a solution more sustainable.

Thanks to Greenpeace and many other media the focus on being Green is a checklist for a data center.

I wrote the above. Below is what Generative AI wrote.

Green Data Center Solutions
In recent years, the concept of green data centers has gained significant traction in the tech industry. As organizations strive to reduce their carbon footprint and operate more sustainably, the importance of environmentally-friendly practices within data centers cannot be overstated.
Green data centers aim to minimize energy consumption and maximize efficiency through various strategies. These include the use of energy-efficient hardware, such as servers and cooling systems, as well as the implementation of innovative cooling techniques like liquid cooling and free air cooling. Additionally, renewable energy sources such as solar and wind power are increasingly being integrated into data center operations to further reduce reliance on fossil fuels.
By optimizing energy usage and investing in sustainable technologies, green data centers not only benefit the environment but also help organizations save on operational costs in the long run. Furthermore, these initiatives contribute to a positive corporate image, showcasing a commitment to environmental responsibility and long-term sustainability.
As the demand for data continues to grow exponentially, the implementation of green data center solutions will play a crucial role in building a more sustainable digital infrastructure for the future. Organizations that embrace these practices are not only investing in the planet but also paving the way for a more efficient and environmentally conscious industry.
Implementing green data center solutions is a step in the right direction towards creating a more sustainable future for the tech industry and beyond.

There’s lots of ways to be, as a person. And some people express their deep appreciation in different ways. But one of the ways that I believe people express their appreciation to the rest of humanity is to make something wonderful and put it out there.

And you never meet the people. You never shake their hands. You never hear their story or tell yours. But somehow, in the act of making something with a great deal of care and love, something’s transmitted there. And it’s a way of expressing to the rest of our species our deep appreciation. So we need to be true to who we are and remember what’s really important to us.

—Steve, 2007

This is a different way to see what problems to solve. Spending time striving to make something wonderful can give your life meaning. Try it and you’ll be hooked for life.

Two things that jumps out from reading Urs post. Are the focus on water as part of Google’s climate-conscious data center cooling. Normally people only focus on electricity and cooling for a green data center, and that is where Google is different in that they have had a water strategy from their early efforts.

The other point is

An example of the myth is this slide. What drives this type of news is it creates much more traffic than the reality.

The presentation continued on to cover the reality to contrast the myths. The data that is used to make data centers work. Be energy efficient, use less water. Sometimes the way media write about data centers it is like the data centers of 20 years ago that had little concern about cooling efficiency. PUE did not exist.

The topics covered are in this slide.

The 7x24 Exchange hit a record 992 attendees and many are still here on the last day at 8:45a in the morning because a presentation by Mark and Bill can only be absorbed by sitting at the conference.

There were 33 slides in the presentation with lots of graphs and diagrams. Covering five different areas.

Given Meta’s openness much of this information is available on their web site. https://sustainability.fb.com/water/

The panel has Greg Stover (Vertiv), John Gross (J.M. Gross Engineering), Peter Panfil (Vertiv), and Zachary Smith (Equinix).

The panel discussion worked well to discuss the issue of open standards like OCP and OPEN 19 and its impact on the industry. To give you an idea of what OCP and OPEN 19 work on the following diagram was shared.

If you have interest in these areas here are links. www.opencompute.org and www.open19.org

Admiral Rogers shows why and how periods of turmoil and chaos are often a leader’s greatest opportunity to shepherd transformational change. While people and organizations seem hard-wired to favor stability and resist change, necessity forces the hand of invention. According to multiple analysts and headlines, the global pandemic accelerated the digital transformation of entire industries by three to seven years—and that window of opportunity is still open—not only for technological change but for shifts in organizational culture and the future of work. Admiral Rogers shows leaders how to embrace the power of chaos and fully leverage its momentum for change.

The presentation was well received and the following are some notes. If you are a person who likes to read a full document you can check out this document that has overlap with what is presented. One of the key points Mike made is described in the document.

Don’t Waste a Crisis

“It’s easy to be a leader when things are going right. It’s easy to be a leader when everyone’s happy. It’s easy to be a leader when there’s tons of resources,” Rogers said. “Where you make your money as a leader, I always thought, is when none of that is true.” But troubled times, if used properly, can lead to breakthroughs, especially in bureaucracies. “Crisis also is opportunity,” he said. “Never let a crisis go to waste.”

Here are a few other snippets from Mike’s presentation.

Leadership in the chaos and uncertainty is the future. Significant change, uncertainty with change, under stress will be common

What is the basic work model at work, at home, hybrid is an example of the unknown.

Our workforce has different expectations than the past. Employee empathy is critical for success

Young generation does not the fear of uncertainty that the older generation does, and don’t assume people will stick around because you fear going to an uncertain future.

How do we create organizational structures that are not one size fits all.

People are more willing than ever to walk away from work.

If you do not focused motivated people you will not be successful.

Human capital is critical component.

Build a work force to sustain over a period of time.

The rate of change is accelerating.

“Never let a crisis go to waste. Use it to drive change.” Vehicle of opportunity

What is regulatory environment in 2030 and beyond?

How do you build in flexibility to accommodate the change?

Blend the best of the cultures from civilian and government is a goal of Admiral Mike Rogers when he ran the NSA. Stifling of communication limits the development of best cultures.

Make choices that leave options.

Congratulations to the 7x24 Exchange team.