AI’s Raised Bar Paradox

Or: I Dream of Gini

Quality is never an accident; it is always the result of intelligent effort. —John Ruskin

The best way to keep a prisoner from escaping is to make sure he never knows he's in prison. —Fyodor Dostoyevsky

Above: Inequality, plotted.

---

ChatGPT just wrote 1,000 blog-posts while you made your cup of morning joe. Now what?

AI turns mediocre into mass-produced and excellence into an arms race. Good-enough becomes automatic, while exceptional is pushed to superhuman heights. In the greatest Red Queen’s race of all time, standing still means sliding backward.

In a world where mediocrity is abundant and worthless, genuine craft becomes premium.

This turns two bits of folk wisdom on their heads:

• “Just do it.” Not anymore. Just doing it means joining a landfill of half-baked content

• “Done is better than perfect.” In an AI glut, excellent is the only thing that still pays

In this world, “ready, aim, fire” beats “ready, fire, aim.” It’s not enough to just throw things at the wall; you have to pick the right wall first.

Or, in the words of Abraham Lincoln, “Give me six hours to chop down a tree and I will spend the first four sharpening the axe.”

Call the coming shock the Gini Spike.¹ But this inequality jolt is measured not only in dollars, but also cognitive autonomy.

As Adam Singer wrote:

A 206-page research paper [entitled Your Brain on ChatGPT: Accumulation of Cognitive Debt when Using an AI Assistant for Essay Writing Task states:] "LLM users consistently underperformed at neural, linguistic, and behavioral levels." This study found LLM dependence weakens the writer’s own neural and linguistic fingerprints. Relying on EEG, text mining, and a cross-over session, the authors show keeping some AI-free practice time protects memory circuits and encourages richer language even when a tool is later reintroduced.

In this future, it’s not just about who has capital, but who retains the capacity for independent thought and creative problem-solving.

The Gini Spike Mechanisms

1. Cognitive Hollowing Out: AI is gutting middle-tier knowledge work, the traditional backbone of economic mobility.

2. Winner-Take-All Dynamics: Those who maintain their creative and analytical edge will command premium wages, while those who become AI-dependent will see their skills commoditized and wages compressed.

3. Capital Concentration: tools are expensive to develop and deploy at scale, concentrating wealth among those who own the infrastructure rather than those who use it.

4. Network Effects: The best AI-augmented workers will increasingly dominate their fields, creating superstar effects where small advantages compound into massive inequalities. The proverbial 10X engineer becomes a 10,000X engineer.

In this brave new world, the disenfranchised lose not only their jobs, but also their ability to think independently, making them structurally dependent on AI systems and, by extension, those who control them.

When creativity turns into prompt-engineering, the true scarce resource becomes unprompted imagination.

So, in the words of Steven Tyler, “Dream On.”

---

Per my about page, White Noise is a work of experimentation. I view it as a sort of thinking aloud, a stress testing of my nascent ideas. Through it, I hope to sharpen my opinions against the whetstone of other people’s feedback, commentary, and input.

If you want to discuss any of the ideas or musings mentioned above or have any books, papers, or links that you think would be interesting to share on a future edition of White Noise, please reach out to me by replying to this email or following me on Twitter X.

With sincere gratitude,

Tom

1. The Gini coefficient is a statistical measure of income or wealth inequality within a population, ranging from 0 (perfect equality, where everyone has the same income) to 1 (perfect inequality, where one person has all the income). It is widely used to compare inequality across countries or over time.