Something Big Is Happening.

Over the last couple of weeks, a phrase has been circulating across podcasts, executive briefings, and LinkedIn feeds. It began with an essay by Matt Shumer that went viral in early 2026. The title was direct: “Something Big Is Happening.” His argument was that artificial intelligence has crossed a meaningful threshold. Not theoretical. Not experimental. Operational.

This is not about holograms, CES robots folding your clothes. It is about real work being reshaped in real time.

When I read it, I did not feel alarmed. I felt recognition.

This is what I have been talking about.

Not fear. Not hype. Pattern recognition.

Technology is moving quickly. Business models are not moving at the same pace. Colleges are not redesigning curricula at the same velocity. Workforce development systems are not recalibrating fast enough. That gap between technological capability and institutional adaptation is where disruption lives.

And that gap is widening.

Artificial intelligence is no longer something you open in a browser to experiment with. It is embedded into infrastructure. Customer relationship platforms draft outreach before a salesperson types a word. Engineering copilots generate code scaffolding and debug in seconds. Meeting platforms summarize conversations automatically. Resume filters narrow applicant pools before a hiring manager reviews them.

These tools are not futuristic. They are operational.

The change is quiet but structural.

When workflows compress, job design changes. When job design changes, career pathways shift. And when career pathways shift, opportunity shifts.

Some jobs will shrink. Certain tasks will be absorbed. Entry-level work may look very different five years from now than it did five years ago. That does not mean professions vanish overnight. It means the composition of work changes.

Repetitive, predictable, pattern-based tasks are the most vulnerable. Early-stage coding, standardized drafting, templated analysis, scheduling, and administrative coordination can now be executed by systems in minutes. That reality changes how organizations staff teams and how they define value.

But what is not at risk is what humans do uniquely well.

Humans create context. Humans exercise judgment. Humans navigate ambiguity. Humans interpret nuance, read rooms, connect across disciplines, and make ethical decisions under uncertainty. AI can generate outputs. It cannot own consequences.

This moment matters precisely because it is not loud. We are not seeing headlines about mass displacement tied directly to automation. We are seeing something more subtle. Work is being redesigned from the inside out.

Talk to recent college graduates. Many are struggling to land entry-level roles that once functioned as training grounds. Some of that reflects macroeconomic cycles. Some of it reflects a deeper structural shift. If AI absorbs foundational tasks, organizations may hire fewer junior staff and expect higher baseline competence from day one.

That is not an inevitable crisis. It is a design challenge.

If entry-level tasks shrink, leadership pipelines must be redesigned intentionally. Mentorship cannot be accidental. Experiential learning cannot be assumed. Skill development must be structured.

This is where preparation becomes essential.

My background in emergency management shaped how I think about change. Preparation is not panic. Preparation is leadership. You do not wait until a hurricane makes landfall to secure infrastructure. You monitor signals early. You model possible scenarios. You act before impact becomes visible to everyone else.

Right now, the signals are clear.

AI is moving from tool to infrastructure. From assistant to embedded workflow layer. From optional enhancement to baseline expectation.

The risk is not that AI becomes powerful.

The risk is that leaders underestimate the speed of integration.

Many organizations remain structured around headcount growth, manual bottlenecks, and career ladders designed for a different era. If productivity per employee increases, compensation models will shift. If junior tasks shrink, evaluation metrics must evolve. If output accelerates, performance standards will rise.

We cannot treat this as distant.

But we also do not need to treat it as doom.

We need clarity.

For individuals, that means moving beyond task ownership and toward value ownership. It means developing durable human capabilities: strategic thinking, ethical reasoning, cultural fluency, creativity, communication, and systems thinking. Technical fluency matters, but it is not sufficient. The professionals who thrive will be those who integrate technology and elevate judgment.

For organizations, it means auditing where AI is already embedded. It means redesigning roles so humans operate where discernment and accountability matter most. It means investing in AI literacy across functions, not only within technical teams.

It also means confronting bias directly.

AI does not remove bias. It can scale it. If historical hiring data favored certain institutions, profiles, or communication styles, and that data trains automated systems, inequity can become embedded at speed. Equity in the age of AI is not a training initiative. It is a design specification. The architecture of systems determines how opportunity flows.

The essay that sparked this conversation may overstate certain timelines. Several credible researchers have pushed back on its most aggressive projections. That debate is healthy. But dismissing the broader signal would be a mistake.

Something big IS happening.

Not as a sudden collapse, but as a steady compression of work.

The tea leaves are visible. The velocity is measurable. The integration is real.

The question is not whether artificial intelligence will reshape the future of work.

The question is whether we will intentionally shape that future.

Readiness is not fear. It is stewardship.

AI will not eliminate leadership. It will expose it.

Leadership is not tested in moments of comfort. It is defined in moments of transition. And this is one of them.