Categories
Blogs/Weblogs Writing

Notes for a Distant Shore

I spend an embarrassing amount of time trying to control how people hear me. Most of us do. We want to be understood, neatly categorized, and told we make sense. But sitting down to actually write and sharing publicly requires dropping all of that. You just have to surrender.

Richard Rhodes nailed the feeling:

“To write is always to seal notes into bottles and cast them adrift at sea; you never know where your notes will drift and who will read them.”

You’re basically bottling up whatever is rattling around in your head on a Tuesday afternoon, tossing it into the digital ocean, and walking away. It’s vulnerable. Honestly, it’s a little reckless.

Once the bottle leaves your hand, you lose your voice. You can’t tap the reader on the shoulder to explain what a sentence really meant. The person who finds it brings their own weather to the shore. They might read a lifeline into a paragraph you barely thought about, or miss your main point entirely because they were distracted by the tide.

Forget about engagement metrics. The connections that actually matter rarely show up on a dashboard anyway. You write something, and it drifts. Maybe for years. Then someone stumbles over it exactly when they need it. You aren’t writing for a demographic; you’re writing for some random person walking the beach. True serendipity.

In the end, you just have to trust the water. Even if the bottle sinks, the act of throwing it is usually satisfying enough.

“Write as if you were dying. At the same time, assume you write for an audience consisting solely of terminal patients. That is, after all, the case. What would you begin writing if you knew you would die soon? What could you say to a dying person that would not enrage by its triviality?” (Annie Dillard, The Writing Life)

Categories
AI Work

The Centaur’s Dilemma: What Chess Teaches Us About the AI Era

Note: this post was stimulated by a recent conversation between Dario Amedei and Ross Douthat.

In 1998, Garry Kasparov did something unexpected after his historic defeat to IBM’s Deep Blue: he teamed up with the machine. He pioneered “Centaur Chess,” a hybrid format where human intuition merges with cold, silicon calculation. The human acts as the executive, the engine as the raw horsepower. For a time, it was the highest level of chess ever played.

But there is a sobering lesson hidden in the evolution of this game. We are currently living through the workforce equivalent of the Centaur era, and history suggests our “hybrid honeymoon” won’t last forever.

Right now, we are in the augmentation phase. A junior copywriter or coder armed with a Large Language Model can suddenly produce work at a staggering pace. The AI acts as a great equalizer, much like a mediocre chess player with a strong engine beating a Grandmaster in the early 2000s. We are shifting into executive roles—prompting, curating, and orchestrating rather than creating from scratch.

However, in modern Centaur Chess, a chilling reality has emerged: human intervention now yields negative returns. The engines have become so impossibly advanced that when a human overrides Stockfish today, they are almost certainly making a mistake. The human loop, once the ultimate strategic advantage, has become a liability.

This is the “Grandmaster Floor” problem, and it is coming for the job market.

“Eventually, companies may view human oversight not as a ‘value add,’ but as an insurance cost they’d rather cut.”

We are seeing this fracture already. Pure “engine” industries—entry-level data analysis, logistical tracking, basic customer support—are rapidly phasing out the human element because human latency is a drag on the system. Yet, in fields requiring high-stakes moral judgment or empathy, like healthcare or law, the Centaur model remains deeply necessary.

This forces a deeply personal question: How do we stay relevant when the engine eventually solves the game?

The answer lies in recognizing the boundaries of the board. Chess is a closed, finite system. Human life and business are open, messy, and infinitely complex. The survival strategy isn’t to compete on calculation, but to double down on connection, empathy, and problem definition. AI is brilliant at providing the perfect answer, but it fundamentally lacks the soul to know which questions are worth asking.

In the future, the human touch won’t just be a necessity; it will be a luxury. The most valuable skill won’t be navigating the engine, but deciding where the engine should go.

A couple of considerations:

• Take an honest look at your daily work: how much of your time is spent “calculating” (tasks an engine will soon do better) versus “evaluating” (deciding what actually matters)?

• If the technical, process-driven aspects of your job were completely automated tomorrow, what uniquely human value—empathy, context, or connection—would you still bring to the table?