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AI-Native Tiers

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title
AI-Native Tiers
type
concept
summary
Six-tier competence ladder for working with LLMs — writing buddy, meeting partner, thinking partner, builder, shipping to prod, agents
tags
llm-adoption, career, ai-workflow
created
2026-05-22
updated
2026-05-22

Six-tier ladder for how someone uses LLMs day-to-day, due to Elena Verna (elena-verna-job-2027). The framing is non-engineer-shaped — the climb goes from text help to shipping production code — but the rungs survive recasting for engineering use.

The tiers are ordered by what you let the LLM do, not by sophistication of the prompt:

  1. Writing buddy. LLM as editor. Catches unclear sentences, suggests stronger phrasings. Verna's discipline: write your own version first, then have the LLM critique it. Skip that and you're already in cognitive-debt territory.
  2. Meeting partner. LLM as note-taker and after-action coach. Granola-style transcription plus prompts like "If you were my manager, what are my strengths?"
  3. Thinking partner. LLM as adversarial collaborator. The whole point is to defeat ai-sycophancy-loop: prompts that explicitly request disagreement ("What's the strongest counter-argument?"), holes-in-thinking checks, idea-divergence-then-pruning ("Give me 20 ideas, cut to 3").
  4. Builder. LLM builds working artifacts — internal tools, landing pages, prototypes — under user direction. The "vibe coding" tier in engineering vocabulary.
  5. Shipping to prod. LLM-written code goes into the production system. For non-engineers this is the unlock Verna pitches; for engineers this is the simonw-vibe-coding-agentic tier where Willison admitted his own line collapsed.
  6. Agents. Self-running recurring tasks ("every Monday, summarize sales and post to #revenue"). Verna's own caveat: "often require more setup, oversight, and cost than just doing the thing yourself." Consistent with agentic-coding-fatigue, agentic-coding-is-a-trap, and the supervisor-paradox of skill-atrophy-supervision-paradox from the engineering side.

Why the ladder is useful

Most discourse about "AI use" treats it as binary — you adopt or you refuse. The ladder separates two things people conflate: what tier you operate at and whether you operate at all. Someone at Tier 2 every day and Tier 6 never is using AI fundamentally differently from someone at Tier 4-5 daily.

It also makes failure modes locatable. Tier 1 done lazily is cognitive debt. Tier 3 done without the disagreement-prompt is just sycophancy. Tier 5 done without verification is the principal-agent gap from agent-principal-agent-problem. Tier 6 done eagerly is the ceo-ai-psychosis / tokenmaxxing failure pattern.

Where engineering changes the ladder

For engineers, Tier 4 (prototype building) and Tier 5 (shipping to prod) are the same skill at different levels of review discipline. The interesting line for them is between Tier 5 (you review and ship) and Tier 6 (the agent reviews and ships), where the code-review-principal-agent effort-signal collapse hits.

The ladder also flattens at the bottom for engineers: Tiers 1-3 are continuous with how a developer has used Google + Stack Overflow + a rubber duck for two decades, just with better latency. The original novelty in the ladder is at Tiers 4-6.

  • jxnl-codex-maxxing — Jason Liu's operating loop reads as a Tier 5-6 production setup (durable threads, goals, heartbeats, side panel).
  • average-is-all-you-need — Tier 5 democratizes "average" output, which is the real economic effect.
  • dont-outsource-learning — Osmani's "form a hypothesis first, ask for explanation before code" is Tier 3 done as a learning discipline rather than a shortcut.
  • contributor-poker — what you bet on a Tier 6 agent's PR vs a human contributor's first PR.

See also

Linked from