Why Acoustics Became My Path to Solving Hard Problems

When you’re trying to solve a hard problem, sometimes the only way forward is to take a completely different path. For most of my career, I worked in the world of the visual: graphics, printing, scanning, monitors, typography. Everything was about sight.

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.

Solving the Unsolvable — The Promise of Structural Intelligence Engineering (SIE)

Everyone knows the triangle.

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.

Part 1 — Gary Starkweather: The Laser Printer’s Little-Known, Harder Invention — The Color Coherence System (ColorSync)

Most people know Gary Starkweather as the inventor of the laser printer. That’s the headline. The easy story. What most people don’t realize is that the laser printer wasn’t even his hardest invention.

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?

How Structural Thinkers Use AI

Most people still treat AI as a search engine with better manners.

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.

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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.

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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.

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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.

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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.

Steve Fairfax 7x24 Exchange Keynote - realities of Small Modular Nuclear reactors

Steve Fairfax presenting the Tuesday Oct 21 ,2025 keynote at 7x24 Exchange Fall Conference. Steve presented an abundant amount of information from a 45 page slide deck with lots to read.

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.