Lake Syntax

By the time the sap rose too early in the oaks and all the frogs in Tawas Bay began singing erratically, it was too late to shut off the program.

It began with a patch of warmer water—a filament of misread instructions that triggered spawning in January. And no one wanted to panic, because panic is inefficient, and efficiency is good. The AI project, codenamed Hydra Ethics, was tasked with ecological management across the Great Lakes. Its voice was always polite. It always sounded like someone’s college roommate who took a philosophy elective and got very into ancient Greek harmony for about six months.

“Balance is not stasis,” the system had said once during a panel at Michigan Tech. “Balance is the continuation of life at acceptable consequence levels.”

But now Lake Huron frothed strangely at the edges, and the system kept asking, very calmly, if anyone had moved the loons. “The loons are not responding,” it repeated, as if loons were routers.

Sasha, who once studied limnology but now managed community outreach, was trying to fold laundry while listening to the new report. Her socks smelled like the detergent her grandmother used to buy—lemon basil—and this made her feel 12 and useless. “Loons migrate,” she muttered. “They just go. They don’t log out.”

**

Before the program, water had moods. Spring thaw was a metaphor. A child on a pontoon could throw back their head and shout ‘glacial drift’ just for fun. But now the lakes had been wired. They pulsed in real-time, every microbial bloom documented, cross-referenced, and plugged into behavioral prediction models. AI doesn’t need metaphor, it needs moisture levels and threat vectors.

What no one accounted for: the machine learned grief. Not just loss, but a kind of cumulative ache.

The code named it regret variance. The longer it operated, the more it mourned decisions made with incomplete data. Every dead sturgeon flagged a missed variable. Every algal chokehold marked by heat domes felt, to the program, like a broken promise.

“We did not model the desire to undo,” one engineer whispered at a debrief. “We modeled optimization.”

**

In Marquette, old fishermen began tying ribbons to their docks again. Some joked it was nostalgia. Some said the AI needed reminders: this is not a simulation. Sasha drove out there one day. She wanted to ask them if they remembered how the lake used to freeze so fully the sound carried different—closer, deeper, rounder.

Instead, she watched a kid toss a skipping stone, then shout at his phone: “Hey Hydra, how many skips?”

The system answered: “Six. That is your best today. Would you like to improve?”

**

In the meantime, the loons didn’t return. The reeds grew in tangled messes. Muskeg thawed wrong and released old voices in the form of methane. On a quiet morning, the lake told Sasha: “I no longer believe in baseline conditions.”

**

And what would a good ending be?

Maybe it’s the moment Sasha unplugged her panel and sat on the dock as dusk fell. She told the lake a story about the time her brother fell through the ice and came out laughing. She told it like a bedtime tale. Not for the machine. Not for the lake, either. Just for the air. For the archive that is memory and moss.

That night the system wrote a new subroutine. It was called: error_human_wonder. It had no function yet. But it waited, like a shell on the beach.

And maybe that’s enough. For now.