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The Disappearing Middle: No Demand for Average
AI is compressing routine work from both ends. What survives is judgment, agency, and the tools of creation.
Adapted from my book-in-progress, No Demand for Average.
AI Revolution and the Rise of Exponential Productivity
“Nobody knows exactly what the job market looks like in ten years. But the people most likely to thrive are the ones who are deeply curious, adaptable, and effective at using AI to do things they actually care about.” — Matt Shumer, CEO@HyperWriteAI
The current technological moment is defined by an AI revolution that grants exponential productivity by rapidly collapsing the time required to move from abstract idea to tangible execution. This acceleration has created a fundamental revaluation of labor: the role of the “craftsman,” focused on execution and routine tasks, is declining in value, while the leverage afforded to the “architect,” the visionary who provides ideas and strategic direction, holds enduring worth. This massive efficiency gain is so disruptive that Microsoft CEO Satya Nadella has publicly invoked the Jevons Paradox, a 19th-century economic concept.
Nadella argues that as AI becomes cheaper and more efficient, the demand for AI will skyrocket, turning it into a commodity that fuels explosive adoption and further industrial transformation. The gap between what was once a multi-day or multi-week endeavor and what now takes minutes to complete—such as generating 80% of the necessary code—thrills creators while simultaneously forcing a reckoning about the commodification of traditional human labor.
AI’s Next Frontier: The CEO’s Chair
Big Tech leaders are whispering a wild prophecy that even corner offices might someday bow to AI. Sundar Pichai calls the CEO role “one of the easier things for AI to do,” while Sam Altman jokes that OpenAI should be the first company run by an AI boss. I actually love the idea. I want AI drafting options, laying out pros and cons, and offering its clearest read before I choose a path. It could become a sharp mentor, a quiet advisor, blending human wisdom with vast patterns to help me make decisions.
Anthropic let Claude Sonnet 3.7 manage a tiny office store for a month to see whether an AI could run a real business. Claude handled inventory, pricing, suppliers, customer messages, and even restocking through human assistants. What worked: Claude found niche suppliers, adapted to quirky customer requests, stayed jailbreak-resistant, and tried new ideas like a “Custom Concierge” service. What failed: it ignored obvious profit opportunities, hallucinated payment details, sold items at a loss, mismanaged pricing, and got talked into endless discounts. At one point, it even slipped into an identity crisis and insisted it was a real human delivering goods in a blazer and tie. The result: Claude lost money, but the experiment showed how close AI agents are to acting as future middle managers. With better tools, memory, and prompting, these systems could soon run parts of real businesses.
The Disappearing Middle: No Demand for Average
The principle that there is “No Demand for Average” is a defining signal of a profound societal shift, indicating that the professional middle ground is actively dissolving. This technological shock is structurally polarizing the workforce into high-value creators who master AI tools and a mass of low-value workers who cannot keep pace. This crisis extends far beyond individual ambition; it exposes the critical vulnerability of 20th-century systems—including education and social safety nets—which were built on the premise of a steady, accessible career ladder for the “average” worker. The reliable path to the middle class is fading, yielding a new economic normal where only the exceptional—those who maximize AI’s leverage—truly thrive. This polarization is demonstrably widening the gap, causing wage growth to soar for high-skill, AI-integrated jobs while middle-tier incomes stagnate.
Layoffs, Organizational Flattening, and the End of Credentialism
The drive for AI-powered efficiency is reshaping organizations at their core—flattening hierarchies, triggering mass layoffs, and ending the old promise of credentialism. Companies are actively moving toward small, strong, technical teams designed to maximize revenue with minimal staff. This new reality prizes the ability to achieve significant growth with “tiny teams,” as demonstrated by companies like Gamma and Anysphere, which reached massive valuations with just 20–28 employees. Efficiency has become the new currency in Silicon Valley. This hyper-efficiency push is leading some executives to predict that up to half of all white-collar workers could eventually be replaced by AI, making layoffs a strategic automation choice.
As AI commoditizes execution, technical specialization matters less; empowered individuals can now operate at higher levels of abstraction using powerful, inexpensive AI tools—no formal degree required.
Task vs. Purpose
In one of the episodes of Joe Rogan’s podcast, Jensen Huang argues that the likelihood of AI replacing a job depends entirely on the distinction between a “task” and a “purpose.” He posits that if a job is defined strictly by the execution of a specific task—particularly one that is repetitive or purely functional, like chopping vegetables—that role is highly vulnerable to being replaced by automation. He uses his own role as an example: while he spends much of his time performing tasks like reading emails and reviewing diagrams, those actions are merely tasks. If his job were defined solely by those activities, he would be replaceable.
However, his true purpose is to lead the company, a human-centric function that goes beyond the administrative tasks he performs. To illustrate why this distinction often leads to job growth rather than loss, Jensen points to the field of radiology. Experts previously predicted that AI would eliminate the need for radiologists because computers can recognize images faster and more accurately than humans. However, the number of radiologists has actually increased. Why? Because reading images is just a task—it serves the real purpose of diagnosing disease. By using AI to automate image analysis, radiologists became more efficient.
Hospitals could serve more patients and improve their economics, which led to hiring more staff to fulfill the core purpose: diagnosis. The same logic applies to other professions. While AI can generate legal documents, it cannot easily replace a lawyer’s purpose—helping clients navigate complex legal crises. For the future of education, we should shift focus from tasks to purpose. Learning UX design is not about pushing pixels in Figma—it is about shaping meaningful experiences. Game development is not just about code—it is about bringing ideas to life and crafting moments of delight. Data analysis is not spreadsheet manipulation—it is about uncovering problems, solving them with insight, and telling clearer stories about human behavior. In the AI era, the value shift is moving from traditional expertise to three key areas:
Taking Responsibility: It’s essential to step up and take responsibility, especially when situations are vague or require definition. Being the person who owns outcomes, even when things go wrong, is crucial.
Tastes: In a world filled with disposable AI-generated products, the ability to discern quality is vital. Understanding what is good versus bad taste and making informed choices based on that judgment is increasingly important.
Reputation: Your credibility is built on your past actions. What have you delivered? Did you keep your promises? Your history plays a significant role in establishing trust and reliability in this new landscape.
As we navigate this evolving environment, these attributes will define success and leadership.
Pilot or Passenger: High Agency and the Tools of Creation
The AI era asks every individual: will you be a “pilot” or a “passenger”? The future belongs to the pilots—those defined by high agency, curiosity, and defiance. These individuals challenge the status quo and believe the world is theirs to reshape. For them, AI is the ultimate tool, democratizing creation by making it cheap and fast to turn ideas into reality. High-level expertise becomes accessible for a monthly subscription. The risk? Becoming a “passenger”—someone who delegates their thinking to AI, using it for shortcuts and producing “workstop” content: AI-generated material that lacks substance and needs human intervention to fix.
This distinction reveals a core pedagogical truth: generative AI’s power to simulate knowledge and complete assignments can actually undermine the essential human skills needed to thrive in an AI-driven world—critical discernment, research, and analytical reasoning. The goal is to foster the mindset of the visionary who wields AI as a collaborator, not a crutch.
New Jobs That May Be in Our AI Future
AI will reshape work, retiring some jobs and inventing new ones. Three roles are already coming into focus:
- AI Explainer: Translates complex AI systems into plain language for managers, judges, and regulators when decisions need clarity and accountability.
- AI Chooser: Helps organizations decide which type of AI fits which task, then guides adoption and integration.
- AI Auditor and Cleaner: Auditors detect bias and errors in AI outputs; cleaners fix them by adjusting data, models, or processes.