Playing Tetris at Every Level

A Systems View of AI Disruption

This essay was contributed by Magda Jagucka Miller. Magda helps media and tech companies navigate change, guiding organizational transformation and AI integration with practical, strategic insight.

At a recent career roundtable, senior computer science and cinematography students asked me what skills they should focus on to stay relevant. Entry-level jobs, they’d heard, are disappearing. Years of expertise could be obsolete the moment they graduate or worse, reduced to a one-click AI task.

We landed on an important truth: fundamentals still matter. For engineering systems or crafting stories, the core principles don’t vanish. What changes is the stack. It’s your craft plus knowing how AI tackles similar tasks. Add curiosity, critical thinking, creative problem-solving, and a willingness to experiment, and you have a toolkit that can navigate uncertainty. By the end of the conversation, the mood shifted—lighter, more hopeful.

But I realized entry-level roles aren’t the only ones impacted. AI disruption doesn’t start and stop at the bottom; it hits every level.

Think about Tetris. Clear a line, any line, and it disappears. Everything above drops. The board reorganizes. That’s AI’s impact today, and most leaders are figuring out which level to play.

This pattern follows what ecologists call the adaptive cycle, a model developed by C.S. Holling to understand how complex systems move through phases of breakdown and renewal. Originally used to study ecosystem dynamics, it reveals something crucial about organizational transformation.

The Release Phase: Which Lines Are Clearing

Disruption unfolds in phases. First comes the sudden breakdown of established structures.

Bottom of the board (junior roles): Research, first drafts, data entry, routine production. These roles are getting questioned the most, assuming AI will replace them. In practice, this removes the training ground for future expertise.

Middle of the board (mid-level roles): Synthesis, coordination, project management. Flattened structures can scale out effectively—but if done poorly, this layer jams, creating bottlenecks that halt growth.

Top of the board (senior roles): Strategic work compresses. Some organizations eliminate senior layers, consolidating oversight. AI may act as a strategic advisor, but stretched leaders risk losing impact if middle-level support disappears.

Clear a line at any height, and everything above drops. Because the system is interconnected, the release phase is forcing us to see what’s breaking so we can rebuild intentionally.

The Reorganization Phase: Generative Chaos

Release gets the marketing hype. But the real work is reorganizing the messy gap between breakdown and whatever stabilizes next. Here, your choices—what to preserve, what to redesign, what to experiment with—shape the system that locks in.

Two patterns emerge:

Reactive compression: Fewer people do the same work, adding AI on top. Pressure stacks until the board jams.

Intentional reorganization: Leaders rethink the whole system. They ask: What must each level do now? How do people develop expertise without foundational tasks? What’s the new path from entry to senior? Where do humans create distinct value?

If this phase feels chaotic, it’s because it is—destruction and emergence coexist. The most common mistake is trying to fix one level at a time.

The Multi-Level Reorganization Challenge

Consider what happened at Duolingo. The language-learning platform reduced contractor translators and content writers, leaning into AI for content generation and localization tasks. The move sparked employee, user, and community backlash over quality and ethics—a loss of trust that pressured the company to reconsider the pace of their human-to-AI transition. They targeted one layer—the specialists who ensured linguistic and cultural accuracy—without rethinking how the system worked as a whole.

The lesson from Duolingo: eliminating experts doesn’t eliminate the need for expertise. It just surfaces later, more expensively, and often publicly.

The same pattern plays out across every level. Cutting entry-level roles doesn’t eliminate entry work—it re-emerges in a different form. Middle layers carry institutional knowledge and connect junior execution to senior strategy. Hollow them out, and you break knowledge transfer. When senior leaders get compressed, they absorb mid-level work due to lost support, and their high-cost time isn’t fully leveraged.

The alternative is to redesign the whole system by rethinking what each level exists for and where human value truly lies. Whether you’re running a streaming platform, a production company, or a creator business, the challenge is the same: intentional system redesign.

What Intentional System Reorganization Looks Like

Junior roles: Develop judgment early, operate at higher levels, direct AI, and make meaningful decisions. Autonomy and impact attract younger generations.

Mid-level roles: Act as strategic translators between AI and human needs. Replace routine coordination with synthesis and judgment.

Senior roles: Focus on strategy, complex decision-making, and high-stakes choices. Freed capacity is used for work only experienced humans can do.

New pathways: With the old ladder gone, advancement becomes portfolio-based. Demonstrated judgment replaces tenure; high performers progress faster.

Organizations that redesign this way will emerge more capable and resilient against future disruption.

What Gets Preserved Matters

Success depends as much on what you preserve as on what you change:

Institutional knowledge: Codify what matters before roles disappear. Build new vessels for knowledge transfer.

Human connection: Mentorship, collaboration, trust. AI can’t replicate these networks.

Cultivating discernment: Evaluating quality, spotting patterns, making decisions under uncertainty. This is the new currency.

Cultural coherence: Values, decision-making norms, and uncertainty tolerance determine whether reorganization is intentional or reactive.

Ignoring preservation risks losing capabilities that will cost far more to rebuild later.

The Exploitation Phase: What Locks In

Next comes exploitation: growth along new pathways. Experiments become standard; provisional practices become permanent.

Organizations that reorganized intentionally will have:

• Talent development models integrating traditional and AI-driven skills

• People capable at the AI–human frontier

• Institutional knowledge preserved in new forms

• Adaptive capacity for future disruption

Suddenly your Tetris board is dropping new shapes. It feels different, but you’re still playing the same game—the fundamentals haven’t changed.

The Conservation Phase: Living With What You Built

Eventually, systems reach conservation: structure solidifies, roles stabilize, patterns harden. Before AI, most organizations lived here—predictable and orderly.

AI pulled us back into release and reorganization. Conservation won’t return for a few years, and when it does, it will follow entirely new principles.

Efficiency-driven organizations conserve scarcity: tight staffing, minimal capacity, constant pressure.

Capability-driven organizations conserve abundance: diverse talent, flexible roles, capacity to absorb change.

The choice happening now is in every small decision about who to keep, how to restructure, what to value, and how to develop people.

The Pattern You’re Building

In Tetris, lines clear at multiple levels. You can’t control the collapse, but as long as you’re clearing the board, you’re still running the game.

Systems change theory is clear: reorganization happens, and that’s what we are experiencing right now. High uncertainty surrounds us, but within it lies opportunity. What you preserve, experiment with, and build today crystallizes into the system you’ll operate in tomorrow.

No matter your level, roles are being redefined with new expectations for both hard and soft skills. If entry-level positions feel scarce or unclear, it’s temporary. Clarity will emerge, and new opportunities will follow.