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Mehmet TURAÇ
Mehmet TURAÇ

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Back to Code | Ep 01: The Invoice of Illusion and the Black Friday Crash

#ai #programming #typescript #architecture

The 15-week technical battle of LogiFlow — a company waking up from the illusion created by artificial intelligence and returning to real engineering.

The Story

The air in LogiFlow's glass-walled main conference room was so tense that even the hum of the air conditioning was inaudible. The projector at the center of the table displayed a single slide for the Board: November Infrastructure and AI API Costs.

The number: $114,500.

CTO Kerem loosened his tie and swallowed hard. "As you can see, our 'AI-First' strategy increased our development speed by 400%. We stood up the Routing microservice from scratch in just 3 weeks using autonomous AI agents..."

The CFO standing at the head cut him off: "Kerem, we get the speed. But this bill costs as much as a car. And last night's Black Friday load simulation crashed the system. Why did your 'flawless' AI give us a 4-hour outage?"

Staff Engineer Defne, sitting at the far end of the table, slowly closed her laptop lid. The time had come to say what she'd been holding back for months — afraid of being branded 'anti-innovation.'

"Because Kerem," Defne said calmly. "AI didn't write us code. AI sold us an illusion that looked like it worked. And that illusion understands nothing about concurrency."

Technical Autopsy

The Datadog logs on screen were flashing red. The problem was in route-optimizer-v2 — AI's proudly written service. This service recalculated truck routes based on real-time traffic data and wrote them to the PostgreSQL database.

The TypeScript code AI produced was nothing short of poetry. Variable names were perfect, JSDoc comments were immaculate, you couldn't get a single ESLint error.

// "Flawless" Code Generated by AI
export async function assignRoutesInParallel(
  truckIds: string[], region: string
) {
  try {
    const results = await Promise.all(
      truckIds.map(async (truckId) => {
        const optimalRoute = await calculateOptimalRoute(
          truckId, region
        );
        await updateTruckRouteInDB(truckId, optimalRoute);
        return { truckId, status: 'success' };
      })
    );
    return results;
  } catch (error) {
    logger.error('Error during route assignment', { error });
    throw new RoutingException('Bulk route assignment failed');
  }
}
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Kerem looked at the screen. "What's wrong with this? It runs operations in parallel with Promise.all. In the simulation, it processed 50,000 trucks in 2 seconds."

Defne smiled bitterly. "Yes, processed in 2 seconds. But we never asked what happened to the database." She filtered the Datadog log:

{
  "timestamp": "2026-11-24T23:45:12.114Z",
  "level": "ERROR",
  "service": "route-optimizer-v2",
  "error": {
    "code": "40P01",
    "message": "deadlock detected",
    "detail": "Process 1245 waits for ShareLock on transaction 8892; blocked by process 1246. Process 1246 waits for ShareLock on transaction 8891; blocked by process 1245."
  }
}
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What the Machine Cannot Comprehend: Physical Limits

"Deadlock," said Defne. "Race condition and connection pool exhaustion."

  1. When AI said Promise.all for 50,000 trucks, Node.js instantly spawned 50,000 async operations.
  2. Each operation went to the database with a SELECT, then modified the route with an UPDATE.
  3. PostgreSQL's max_connections limit was 100. PgBouncer pool size was 50.
  4. 50,000 requests piled into a pool of 50.
  5. Even worse — AI hadn't used a transaction block when writing the UPDATE query.

"AI knows code syntax and the happy path. But AI doesn't feel PostgreSQL's MVCC architecture, the connection pool limits in RAM, or disk I/O bottlenecks. Our craft is managing these physical limits."

Lessons from Episode 1

1. AI Concurrency Blindness: LLMs don't account for the physical limits of system resources when writing async code. Rate-limiting and batching mechanisms must always be added by humans.

2. Transaction Boundaries: AI writes database operations with a "happy path" focus. Leaving lock mechanisms like BEGIN, COMMIT, ROLLBACK, and SELECT ... FOR UPDATE to AI results in production deadlocks.

3. Vanity Metrics: Writing 1,000 lines of code per day with AI can mean writing 1,000 lines of technical debt per day.

This is Episode 1 of the "Back to Code" series. Next up: Episode 2 — Autopsy: The Lie That Looked Flawless.

Series: back.to.code · 2026

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