The emotional issue with VibeCoding

What this episode covers

Summary follows (Spotify bans us from pasting the full transcript)Whether or not you personally identify with “vibe coding,” you’re probably, at this point, using some variation of agentic coding. It’s everywhere now. It’s in the IDE. It’s on the desktop. It’s in the cloud. It’s attached to repos. It’s quietly becoming the default way code gets written.The volume of code you’re writing now is conservatively five times the volume of code you were writing three years ago.I want to talk about the knock-on effects. The side effects. The bit I don’t think people are really discussing.## Threaded Index (Narrative Map)1. **AI coding isn’t just faster — it changes how many things you can attempt**2. **Shepherded vs fully agentic coding: where most people actually live**3. **The real productivity multipliers (5×, 10×, 20×) are already here**4. **Why senior engineers and CTOs are coding again**6. **The “graveyard curve” of unfinished AI projects**7. **Psychological fallout: identity, shame, and organizational denial**8. **Why code quality is no longer the bottleneck**9. **The real bottleneck: everything around coding**### 1. AI coding is already agentic — whether you call it that or notMost people live in the middle: **shepherded coding**.You watch the AI, accept or reject changes, course-correct, override sections, and sometimes just type the solution yourself because it’s faster than explaining the fix.**Key point:** This range is fluid. Everyone moves back and forth across it.### 2. Code volume has exploded — and that’s the baseline nowMost developers are writing **at least 5× more code** than they were three years ago.If you’re only seeing a 10–20% improvement, you’re now in the minority — and probably not using AI effectively.### 3. The multipliers are real — and they’re not hypeIn my own work — commercial, personal, greenfield, and legacy — I regularly see:* **5× overall** as a floor* **10×** cases where days replace months### 4. This has fundamentally changed who writes code* CxOs and senior leaders who never coded* Engineers who hadn’t written code in years* Non-technical people writing production code### 6. The part nobody is talking about: cognitive overloadWhen one person can run **5–6 projects in parallel**, a new problem emerges.The emotional and psychological burden of:* Constant context switching* Multiple incompatible problem domains* Different codebases, architectures, states of progress…becomes overwhelming.### 7. The “graveyard curve”1. **Early phase** One AI-powered project ships fast. The ROI feels magical.2. **Acceleration phase** You take on more projects.3. **Warning phase** You become the bottleneck.4. **Collapse phase** Months later: * No projects are shipping * Side projects are stalledYou now have:* Six half-finished projects* Ten more barely started* No clarity on why any of them stopped### 8. Why projects stall permanentlyEach project hits a small speed bump.Individually, manageable.Collectively, impossible.### 9. The psychological falloutRepeated failure to deliver — even on self-promises — creates shame.People hide:* From colleagues* From managers* From themselvesAnd this is happening **everywhere**, largely unspoken.### 10. Why optimizing code quality is the wrong fightSince roughly ChatGPT-4, **code quality has not been the limiting factor**.The real bottleneck is everything *around* coding.### 11. What can we do? (No silver bullet)There’s no universal answer. But some directions are clear:* Stop optimizing code generation* Start optimizing **shipping capacity*** Actively limit parallel projects* Borrow from corporate innovation models that manage many experiments without overload* Explore “AI operating system” approaches that use AI to manage the meta-work

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