The copilot delusion

Management pressure and AI hype

• Several commenters describe strong top-down pressure to “use more AI,” including halved estimates, tool-adoption KPIs, and implicit layoff threats.
• This is seen as an “AI-shaped hammer” phase in the hype cycle, where leadership treats AI as a universal cost-cutting tool without technical justification.
• Some suggest unions or structural changes to rebalance power; others darkly joke about replacing management with AI instead.

Productivity gains: strong disagreement

• One camp reports dramatic productivity boosts (up to “months of work in weeks”), entire services and QA roles replaced, and warns skeptics they are “being left behind.”
• Another camp sees modest gains (10–30%) in specific tasks like boilerplate, tests, migrations, and unfamiliar APIs, far from 2–10x claims.
• Skeptics compare the funding/adoption argument to blockchain/NFT bubbles and note that if 10x gains were real, industry-wide effects would be obvious by now.

Code quality, maintenance, and “vibe coding”

• Many worry AI accelerates creation of brittle “shanty towns of code”: it works now, but is harder to maintain, debug, or reason about later.
• Stories include AIs making dubious schema changes, poor indexing, subtle security issues, and “plausible but wrong” fixes that only experts can catch.
• There’s concern that stakeholders care only about short-term output, not long-term reliability, leading to quality crises later.

Learning, expertise, and skill erosion

• Central theme: outsourcing thinking outsources learning. If AI writes the code, juniors don’t build mental models, debugging skills, or system intuition.
• Some see this as elitist gatekeeping; others frame it as basic pedagogy—struggle and failure are how you learn fundamentals.
• Comparisons are made to earlier tools (debuggers, IntelliSense, Stack Overflow). Detractors argue AI is different because it can bypass fundamentals entirely and is an extremely leaky, unreliable abstraction.

Business incentives and non-technical leadership

• Commenters emphasize that many businesses primarily want to reduce expensive engineering headcount, not cultivate craft.
• Non-technical executives’ anxiety about software complexity makes them receptive to promises of AI-driven cost cuts, even when they don’t grasp the risks.

Future trajectory and uncertainty

• Some expect a “quality blowback” similar to other industries where cost-cutting undermined safety/quality; others think most businesses won’t need high-quality software.
• Several note that tools are improving rapidly and today’s criticisms may age poorly, but others warn that the “last 10%” of reliability and understanding could take decades.