The Helm Program: Building Civic Infrastructure for the AI Workforce Transition

In 1865, William Stanley Jevons observed something counterintuitive about coal: improvements in steam engine efficiency didn't reduce coal consumption—they increased it. Better engines made coal useful for more applications, expanding total demand far beyond what efficiency saved. Jevons's Paradox, as this became known, explains why technological advancement that appears to threaten jobs often creates more work than it eliminates.

I've been thinking about Jevons lately—not as economic theory, but as lived experience. For the past decade, I've owned and operated Divergence Academy, a vocational trade school serving career transitioners, particularly veterans moving into civilian IT roles. We teach Cybersecurity, Cloud Engineering, GRC (Governance, Risk, and Compliance), and Intelligent Automation. For ten years, the model worked: take people in transition, teach them technical skills, place them in jobs.

Then, about 24 months ago, my model started breaking.

Not catastrophically. Not overnight. But placement rates began slipping. Employers' requirements shifted in ways my curriculum couldn't keep pace with. The jobs weren't disappearing—they were transforming, and faster than my institutional response time could match. Students were graduating with valuable technical skills but increasingly found themselves competing in a market that now demanded something additional, something harder to name: adaptive capacity in the face of AI-augmented workflows.

When you own the production facility—when declining placement rates hit your bottom line—academic debates about workforce transformation become operational urgencies. You can't afford to wait for someone else to build the infrastructure you need. You build it yourself, or you close.

So I built it. What emerged is what I'm calling The Helm Program, powered by the Scaffold Framework. This article is about that framework, the infrastructure it creates, and why I think it matters for more than just my trade school's survival.

Here's the context that explains why this framework exists: I own three companies, each solving a different piece of the workforce transformation puzzle:

Divergence Academy (10 years) is my vocational trade school. I produce workers—not metaphorically, literally. People come in with military experience or career transitions, I train them in technical domains (Cybersecurity, Cloud, GRC, Intelligent Automation), they leave with certifications and, ideally, jobs. I'm not running a think tank analyzing workforce policy; I'm running an operational business where placement rates and student outcomes determine survival.

Euler Center (18 months in making) is my measurement and evaluation company. As Divergence Academy's needs evolved—particularly my need to assess not just technical skills but adaptive capacity—it became clear I needed measurement infrastructure that didn't exist in the market. So I built it. Euler Center develops frameworks for evaluating the kinds of "soft" skills that AI transformation makes critical: judgment, pattern recognition, contextual decision-making, emotional intelligence in ambiguous situations.

9brains (8+ years) is my AI consulting firm focused on GRID deployments: Campus, Content, Career, and Compliance AI systems. The Helm Program lives here. It's my forward deployment mechanism—the way I get AI operators embedded in organizations, starting with Compliance roles. I'm not selling AI tools; I'm building the human infrastructure that makes AI transformation productive rather than disruptive.

The integration isn't accidental. I can't measure adaptive capacity without understanding what jobs become when AI arrives. I can't train people for transformed roles without measurement frameworks that distinguish pattern work from judgment work. And I can't place my graduates into roles that don't have names yet without building the organizational infrastructure that creates those roles.

Most people trying to solve workforce transformation own one piece: the school, or the assessment tool, or the consulting practice. I own all three because the system requires all three, and none of them worked in isolation during the 24-month period when everything started shifting.

Three weeks ago, I was invited to speak at an industry event where data center infrastructure was the hot topic. The conversation kept circling back to one project: the Advanced Energy and Intelligence Campus being built near Amarillo, Texas—11 gigawatts of IT capacity across 5,800 acres, scheduled to begin delivering power by the end of 2026.

To put that scale in context: 11 gigawatts is enough to power roughly 8 million homes. Instead, it's powering AI data centers—the computational substrate of the transformation we're all talking about.

Here's what struck me: nobody at that event was talking about where the workforce comes from.

They discussed nuclear reactor configurations (four 1-gigawatt Westinghouse AP1000 reactors), natural gas infrastructure (sitting atop the Panhandle Hugoton Gas Field), solar arrays, and battery storage. They debated permitting timelines and grid interconnections. But when I asked about the electrical trade professionals who would build and maintain this infrastructure, I got blank stares.

Then someone mentioned BICSI.

BICSI—Building Industry Consulting Service International—is the global standard for Information and Communications Technology (ICT) infrastructure. Their certifications (RCDD for telecommunications design, DCDC for data centers, Installer programs for fiber and copper cabling) represent the training pipeline for the people who actually build the physical layer that AI runs on. Data centers don't build themselves. Someone has to design the structured cabling systems, install the fiber optics, configure the telecommunications distribution, manage the project timelines.

The Amarillo project will need thousands of BICSI-certified professionals. So will the dozens of similar projects breaking ground across Texas and the Southwest. The infrastructure is being funded. The timelines are aggressive. But the workforce development conversation is barely starting.

This is the gap I'm trying to address with The Helm Program—not just for data center electricians, but for the much larger population of knowledge workers whose jobs are being transformed by the AI infrastructure that places like Amarillo represent.

I'm beginning conversations with BICSI about how Divergence Academy can serve this emerging need. Not because it's an interesting market opportunity (though it is), but because it's the concrete, immediate version of the broader transformation challenge I've been wrestling with: jobs are changing faster than training infrastructure can respond, and somebody has to build the bridge.

Academic research on workforce transformation typically proceeds at the pace of grant cycles and publication timelines. Theory gets developed, pilot programs get funded, papers get peer-reviewed, and maybe five years later some insights filter into practice.

I didn't have five years. I had quarterly placement rate meetings and students whose career transitions couldn't wait for the research to catch up.

Around mid-2023, something shifted in my world. Employers who had reliably hired my Cybersecurity graduates started adding requirements that didn't fit my curriculum: "Must be comfortable with ambiguity." "Proven ability to synthesize across domains." "Experience navigating rapid change." These weren't technical requirements. They were signals that the shape of the work was changing.

Initially, I responded the way schools typically do: I added modules. A unit on "adaptability." Some content on "soft skills." I invited guest speakers to discuss "thriving in uncertainty." It didn't work. My students learned about adaptability but weren't developing adaptive capacity. The distinction matters: knowing about something isn't the same as being able to do it under pressure.

That's when I realized my problem wasn't content—it was diagnostic infrastructure. I couldn't measure what I needed to develop. And I couldn't develop it without first measuring where people actually stood.

This is where Euler Center entered the picture. I needed a framework that could:

The standard tools—personality assessments, skills inventories, aptitude tests—weren't built for this. They measure relatively stable traits, not dynamic capacity for role transformation. I needed something different, so I built it.

The Scaffold Framework emerged from this operational necessity. I'm not presenting it as finished theory or settled science. I'm presenting it as my evolving methodology—one that's working in the specific context of my trade school serving career transitioners, but that I think has broader applicability.

My framework rests on a few core premises:

Every role contains both pattern work (rule-following, template application, structured process execution) and judgment work (ambiguity navigation, contextual decision-making, synthesis across domains). The ratio varies by role, but both are always present.

What I've observed is that AI's impact is asymmetric: It's remarkably good at pattern work and remarkably limited at judgment work. This creates a predictable transformation pattern in my view—roles shift toward higher judgment ratios as AI handles more pattern work.

The transformation isn't elimination; it's evolution. I see tax accountants not disappearing when AI can process returns; they become financial strategists who use AI for the pattern work while focusing on judgment about complex scenarios, client-specific optimization, and regulatory gray areas.