URE — Unified Resilience Engineering#
The human brain is hardwired for the tangible. We understand what we’ve touched, carried, walked through. Ask an American to picture 2.3 kilos of meat and you’ll get a blank stare. Tell a European to walk 80 feet down a corridor instead of counting three doors, and they’ll overshoot it. We don’t process abstract units — we process experience.
Now scale that problem up.
Austin, Texas sprawls across 305 square miles. San José, California covers 180. Each one draws roughly one gigawatt from the grid. That’s an entire city — hospitals, traffic lights, air conditioning, schools, everything humming at once. One gigawatt.
Outside Houston, a single data center campus sits on less than one square mile. It draws the same gigawatt.
Condense everything Austin consumes — every house, every hospital, every streetlight — into a footprint smaller than a neighborhood park. That’s what a hyperscale data center is. And more than a dozen of them are being built across the United States right now.
Want to get serious?
A grizzly bear weighs about 600 pounds. The rat behind your local dumpster weighs about two. That’s a 300-to-1 ratio — roughly the same ratio between the sprawl of Austin, Texas, and the footprint of a single gigawatt data center campus. Except the grizzly doesn’t consume 300 times more oxygen. The data center consumes every watt the city does.
That’s the beast we’ve built. A rat with a grizzly’s appetite — lab-made, power-dense, and nothing in the old playbook was designed to feed it. What goes in as power comes out as heat. Every watt. No exceptions.
This is a physics problem. When you compress a gigawatt into a square mile, everything downstream — power conditioning, thermal capacity, transient management, reliability — behaves differently than anything we’ve operated before. The rules that governed traditional data centers don’t scale to AI infrastructure energy density. The playbooks don’t transfer. The dashboards lie.
There are no playbooks written for the AI era — not for AI Factories, Token Factories, or Deep Training Facilities. We’re talking about ML and RL training jobs spanning thousands of nodes on a typical Tuesday.
For perspective: five years ago, the most powerful computer on Earth was Fugaku — 159,000 nodes, 432 racks, a billion dollars of Japanese national investment, drawing 30 megawatts. Countries bragged about it. Scientists waited in line for access. It was a generational achievement.
Today, a NeoCloud launches a “modest” 300-megawatt facility — ten Fugakus worth of power — and it doesn’t even make headlines.
Physics doesn’t negotiate. URE starts there.
My work — building data centers from scratch in highly constrained environments — shaped a particular vision of how the stack is actually built. Not how it’s drawn in architecture diagrams, but how it behaves under load, under budget pressure, and under the laws of thermodynamics.
You can read more about my background at /stefano-schotten/.
This world isn’t one-size-fits-all. URE connects the seams between operational layers — power, thermal, compute, network, cost, compliance — and turns them into reliable deliverables with real economic value. Not another dashboard. Not another abstraction. A reasoning method built from twenty years of scars for an era that doesn’t yet have playbooks.
I break the infrastructure stack into five operational layers: foundation, AI-era baseline, infrastructure and hardware, software and orchestration, and economics — each one inheriting the failures of the layer below it. That framework is the backbone of everything I publish here.
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This is the first of a three-part series based on a real-world engagement: a company that scaled from $40M to $1B in annual revenue in just five years, and the security program that had to grow with it.
This is a story about building high-performance operating systems where security, standards, architecture, and performance act as enablers rather than constraints.
Part 1: Earning credibility before you’ve earned authority. Part 2: Blurring the lines - Security at the SRE and Operations level. Part 3: Wrapping the gift - Transparency and agency. The Inflection Point A few years back, AMTI was at the heart of a fascinating corporate challenge. I was serving as a fractional CISO and advisor for a company standing at a critical inflection point.
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It was 2017. We had just deployed an additional ScaleIO cluster to handle the onboarding of a new customer with hundreds of VMs. Eight nodes, each with 40 Gbps at the backend. Beautiful. Efficient. The whole rack was a work of art—Dell R740s with MD1220 expansions, bezels removed so you could see all those drives blinking in perfect synchronization.
The cluster was deployed less than two weeks ago. I told the customer to “burn it.”
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A few years ago, I was having dinner with the Americas VP of a European energy supermajor — one of those companies that extracts oil from war zones, negotiates with regimes that don’t appear on polite lists, and operates in places where “political risk” means your assets might get nationalized or your personnel kidnapped.
Seventy-plus countries. Active operations in Libya, Nigeria, Angola, Myanmar, Yemen. The kinds of places where security briefings come before breakfast.
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Context got commoditized. Translation is next.
When my company’s acquisition closed in 2024, I thought about pursuing a psychology degree in the US. The impulse was the same one that drives URE: wanting to understand how things are wired under the hood. My wife shut it down—“Really? You know that’s not going to work”—and she was right, though neither of us fully understood why at the time.
What I was actually chasing wasn’t psychology. It was context.
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A bricked storage array, a 2+4 SLA that technically performed, and a technician asking about lunch while executives circled. We learned that risk transfer is an illusion when your blood is on the floor.
January 2026 · Stefano Schotten
The contract was honored. The business still bled.
My case manager called me from the customer site. I could hear the tension before he said a word.
“The VPs are pacing. Four of them, maybe five. They’re all just… standing around IT, watching.”
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I see people everywhere anxious about whether AI will disrupt their jobs, their industries, their lives. I’ve always approached this with calm. Not indifference—calm.
The future rarely sends advance notice, but it is always arriving. This isn’t news. It’s the human condition.
A few years ago, I attended a keynote by Michio Kaku where he framed—perfectly, for me—the relationship between humanity and technological change. What follows is my version. I can’t claim novelty, and I’m not a domain expert in sociology or economics. I’m an infrastructure builder observing the same pattern from the inside.
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A few months ago I read Project Hail Mary and found myself thinking about observation and agency. Einstein didn’t “invent” spacetime dilation—he created the conditions to perceive it. Without the means to observe, you’re just touching walls in complete darkness. Trial and error, yes, but you never truly know the depth of what you’re sensing.
Saturday mornings I take my son to flag football. He’s been in martial arts for half his life—his coach loves his resilience. But something surfaced in team sports that doesn’t appear on the mat.
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In 2015, I did what seemed like the mature thing to do. I created a Production Engineering department.
My college foundation was production engineering. I was a true believer: if we formalized standards and assigned a dedicated group to own operational rigor, the organization would naturally converge toward consistency.
The mandate: Create SOPs. Define standards. Reduce variance. Improve reliability.
On paper, it was textbook. In practice, it was a slow-motion collision with reality.
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Most security programs are built around preventing bad things from happening. That’s necessary but insufficient. At AMTI, where I served as CTO and led infrastructure security for a multi-tenant cloud serving customers from single-VM deployments to enterprise DRaaS contracts spanning hundreds of miles of metro fiber, I learned that mature security is about resilience: the capacity to detect, contain, and recover faster than adversaries can escalate.
The Visibility Problem at Scale Operating a cloud service provider on your own ASN creates a specific governance challenge: you’re the abuse contact, but in a GDPR-compliant architecture, you have no visibility into customer data. Encrypted traffic is opaque by design. This constraint forced architectural discipline: we couldn’t inspect our way to security, so we had to instrument our way there.
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Every company says security is a core value. Few embed it as a design constraint. The difference shows up when things break.
I get a call from a co-founder I’ve known for years. His company just raised $400M+ Series D. His voice is flat: “We have a problem.” Same day, we’re on a call. He’s a skilled engineer — personally devastated. They leaked over 2 million user records. Home addresses. Phone numbers. The full profile. The data had been publicly accessible for three weeks before anyone noticed.
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