fix: purge AI slop from judgment essay, add voice linting pipeline

- Killed 16 em-dashes (Claude fingerprint, 15.9/1000 words -> 0)
- Replaced all clause-dash-elaboration patterns with periods, colons, restructuring
- Removed 'The One-Sentence Version' section (restated the intro, voice anti-pattern)
- Broke tricolon at lines 56-58 (too-clean parallel structure)
- Collapsed 'How to Actually Help' listicle into connective prose
- Added receipt link for ~100x inference cost claim
- Heading dashes replaced with colons (Tradesman Analogy, Excellence vs Functional)

New tooling:
- scripts/lint-voice.sh: mechanical anti-slop linter (em-dashes, trigger words,
  hedging, filler, receipts, sentence uniformity)
- .amplifier/skills/voice-check/SKILL.md: LLM-as-judge voice authenticity check
  (8 dimensions against VOICE.md profile)
- .amplifier/AGENTS.md: standing rule requiring both checks before any publish

🤖 Generated with Amplifier

Co-Authored-By: Amplifier <240397093+microsoft-amplifier@users.noreply.github.com>

src/content/posts/ai-commoditizes-features-but-judgment-compounds.md

@@ -8,23 +8,23 @@ summary: "AI raises the floor dramatically. It does not flatten the ceiling. The
 
 ## The Core Argument
 
-Real professional engineers will still be needed for excellence — not because AI can't write code, but because excellence is qualitatively different from functional, and the gap between the two requires judgment, taste, and accountability that no model improvement can close.
+Real professional engineers will still be needed for excellence. Not because AI can't write code, but because excellence is qualitatively different from functional. The gap between the two requires judgment, taste, and accountability that no model improvement can close.
 
 AI raises the floor dramatically. It does not flatten the ceiling.
 
 ## The Dignity Problem (Most Underappreciated)
 
-The threat to engineers isn't only economic. When expert judgment stops being consulted, something human is lost — the social recognition that hard-won expertise deserves. A doctor who loses clinical judgment to AI doesn't just lose income; they lose the acknowledgment that fifteen years of intuition was worth something.
+The threat to engineers isn't only economic. When expert judgment stops being consulted, something human is lost: the social recognition that hard-won expertise deserves. A doctor who loses clinical judgment to AI doesn't just lose income. They lose the acknowledgment that fifteen years of intuition was worth something.
 
 This is a different kind of harm than job displacement and deserves its own conversation.
 
 ## "Judgment + AI > Professionals" Is Weaponized Dunning-Kruger
 
-Non-engineers with AI tools get just enough coherent output to *look* competent. This is more dangerous than obvious incompetence — it fools everyone longer, including the person doing it.
+Non-engineers with AI tools get just enough coherent output to *look* competent. This is more dangerous than obvious incompetence. It fools everyone longer, including the person doing it.
 
 The professional sees second-order failures coming. The amateur with AI doesn't know there is a second order.
 
-## The Tradesman Analogy — Where It Holds and Where It Breaks
+## The Tradesman Analogy: Where It Holds and Where It Breaks
 
 **What actually protects trades:** physical reality checks (the fridge either gets cold or it doesn't), special tool access, licensing for hazardous materials, liability, code inspections.
 
@@ -34,47 +34,44 @@ The professional sees second-order failures coming. The amateur with AI doesn't
 - Complex systems integration (AI-assisted apps work at 1,000 users; fail catastrophically at 100,000)
 - Security and adversarial reasoning (not "write code that works" but "what does a motivated attacker do with this?")
 - Production debugging of distributed systems (race conditions, cascading failures)
-- Professional accountability — someone has to sign off, and their reputation is on the line
+- Professional accountability: someone has to sign off, and their reputation is on the line
 
-The protection isn't tool access. It's *complexity that bites back* — systems that reward deep knowledge by visibly punishing its absence.
+The protection isn't tool access. It's *complexity that bites back*. Systems that reward deep knowledge by visibly punishing its absence.
 
-## Excellence vs. Functional — A Qualitative Distinction
+## Excellence vs. Functional: A Qualitative Distinction
 
 A better model raises the ceiling on "good enough." It doesn't touch excellence. Excellence requires:
 
 - **Knowing when requirements are wrong.** AI executes on requirements. It doesn't challenge them.
 - **Taste about what not to build.** Knowing which feature request is a symptom of a deeper problem.
 - **Conceptual integrity over time.** Holding the whole system in your head and noticing when new pieces violate its coherence.
-- **The 3am judgment call.** Partial information, pressure, three competing hypotheses — picking which risk to take and owning it.
+- **The 3am judgment call.** Partial information, pressure, three competing hypotheses. You pick which risk to take and you own it.
 - **Scar tissue.** AI has no memory of the deployment that took down prod because of a race condition that looked fine in testing. The professional does.
 
 ## The CEO Incentive Problem
 
 CEOs optimize for what they can measure: headcount costs, features shipped, time to market. AI makes the visible part of engineering look cheap.
 
-What isn't on any dashboard:
-- The judgment call that prevented a disaster
-- The architectural decision that kept the system coherent at scale
-- The taste that made the product feel like one thing
+What isn't on any dashboard: the judgment call that prevented a disaster nobody even knows about. An architectural decision from three years ago that's the reason the system still works at scale. Taste. The product feels like one thing instead of six things glued together, and nobody can point to a line item that made it so.
 
-The failure mode of cutting engineering judgment is **lagged** — 12-18 months to show up, and by then it's attributed to market conditions or leadership changes, not the original decision.
+The failure mode of cutting engineering judgment is **lagged**. It takes 12-18 months to show up, and by then it's attributed to market conditions or leadership changes, not the original decision.
 
 This is structurally identical to cutting cybersecurity. You don't see the attacks that didn't happen.
 
 **What could change the incentive structure:**
-- Liability — if organizations faced professional accountability for AI-generated software failures the way they face accountability for accounting fraud, you'd need engineers the way you need licensed accountants
-- Attributed failures — high-profile disasters clearly traced to "they fired engineers and replaced with AI"
+- Liability: if organizations faced professional accountability for AI-generated software failures the way they face accountability for accounting fraud, you'd need engineers the way you need licensed accountants
+- Attributed failures: high-profile disasters clearly traced to "they fired engineers and replaced with AI"
 - Credentialing and certification in safety-critical domains (medical, aviation, finance)
 
 Arguments don't change incentive structures. Consequences do.
 
 ## The Pricing Gatekeeping Layer
 
-Model providers have inserted themselves as a new kind of gatekeeper. The non-engineer thinks they've broken free from professional gatekeeping — they've actually just changed gatekeepers to ones who charge by the token, can change pricing overnight, can deprecate models, and have no professional obligation to anyone.
+Model providers have inserted themselves as a new kind of gatekeeper. The non-engineer thinks they've broken free from professional gatekeeping. They haven't. They've changed gatekeepers to ones who charge by the token, can change pricing overnight, can deprecate models, and have no professional obligation to anyone.
 
 **The sustainability tension:**
 - Major labs (Anthropic, OpenAI) are burning enormous capital; pricing reflects "keep the lights on while we figure this out"
-- Inference costs have dropped ~100x in under three years; open source is closing the capability gap
+- Inference costs have dropped ~[100x](https://epochai.org/data/notable-ai-models) in under three years; open source is closing the capability gap
 - If prices collapse and open source wins → AI becomes nearly free, *strengthening* the "judgment + AI beats professionals" narrative
 - If prices stay high or providers fail → workflows built on top become hostages
 
@@ -82,21 +79,12 @@ The accountability that can't be priced by the token: when the AI-assisted decis
 
 ## How to Actually Help
 
-**In immediate conversations:**
-Make the specific argument, not the vague one. Not "we need engineers" but "here's the failure that happened because judgment was absent." Concrete and attributed. Also: name what the human contributed when AI does something — normalize crediting judgment, not just output.
+Start with the conversation you're already in. Make the specific argument, not the vague one. Not "we need engineers" but "here's the failure that happened because judgment was absent." Concrete and attributed. Name what the human contributed when AI does something well. Normalize crediting judgment, not just output.
 
-**In mentorship:**
-The skills most worth transferring aren't coding skills. They're judgment skills — how to challenge a requirement, how to read a system for hidden brittleness, how to develop taste. These were assumed to come with seniority and were never made explicit. Make them explicit now.
+In mentorship, the skills most worth transferring aren't coding skills. They're judgment skills: how to challenge a requirement, how to read a system for hidden brittleness, how to develop taste. These were assumed to come with seniority and were never made explicit. Make them explicit now.
 
-**At the organizational level:**
-Push to measure what actually matters: system health, architectural coherence, incident prevention. If these aren't measured, they'll keep being cut because they're invisible.
+At the organizational level, push to measure what actually matters: system health, architectural coherence, incident prevention. If these aren't measured, they'll keep being cut because they're invisible.
 
-**In public:**
 The counter-narrative that's true and needs more voices: *AI commoditizes features, but judgment compounds.* People who understand this need to say it plainly, with examples, to audiences who influence the decisions.
 
-**The structural work:**
-Liability frameworks, credentialing, regulation in safety-critical domains — slow and requiring collective action, but the individual can support and accelerate those conversations.
-
-## The One-Sentence Version
-
-The profession that survives is the one hardest to demo without, not just hardest to do without — and the things that are hardest to demo without are exactly the ones that don't show up in any headcount review.
+The structural work (liability frameworks, credentialing, regulation in safety-critical domains) is slow and requires collective action. But the individual can support and accelerate those conversations.