How automatable is your job? The Messiness Index scores jobs across 6 dimensions of task complexity. Higher messiness = harder to automate.
---
format: typebulb/v1
name: Messiness Index
---
**code.tsx**
```tsx
import React, { useState } from "react";
import { createRoot } from "react-dom/client";
interface JobAnalysis {
job: string;
scores: {
coordination: number;
knowledge: number;
exceptions: number;
feedback: number;
environment: number;
adversarial: number;
};
notes: string;
}
interface Insight {
jobs: JobAnalysis[];
}
const DIMENSIONS = [
{ key: "coordination", label: "Coord", full: "Coordination", desc: "Complexity involving people, synchronization, time pressure, and relationship depth." },
{ key: "knowledge", label: "Know", full: "Knowledge", desc: "Breadth and depth of domains touched and expertise required." },
{ key: "exceptions", label: "Except", full: "Exceptions", desc: "Frequency, cost, and novelty of unpredictable events or errors." },
{ key: "feedback", label: "Feed", full: "Feedback", desc: "Measurability, speed, and stakeholder agreement on success." },
{ key: "environment", label: "Env", full: "Environment", desc: "Physical variability, accessibility constraints, and required manual precision." },
{ key: "adversarial", label: "Adver", full: "Adversarial", desc: "Degree of active opposition, competitive stakes, and player adaptiveness." },
] as const;
type DimKey = typeof DIMENSIONS[number]["key"];
const SCORE_LABELS: Record<number, string> = {
0: "N/A",
1: "V.Low",
2: "Low",
3: "Medium",
4: "High",
5: "V.High",
};
function scoreColor(score: number): string {
if (score === 0) return "#333";
const colors = ["", "#2d5a27", "#4a7c3f", "#c9a227", "#c46210", "#a82a2a"];
return colors[score] || "#333";
}
function totalMessiness(scores: JobAnalysis["scores"]): number {
return Object.values(scores).reduce((sum, v) => sum + (v || 0), 0);
}
interface TooltipContent {
title: string;
body: React.ReactNode;
color: string;
}
interface TooltipState {
visible: boolean;
x: number;
y: number;
content: TooltipContent | null;
}
function Popup({ state, onClose }: { state: TooltipState; onClose: () => void }) {
if (!state.visible || !state.content) return null;
const { title, body, color } = state.content;
return (
<div
className="popup-container"
style={{ left: state.x, top: state.y, borderColor: color } as any}
>
<div className="popup-header">
<span className="popup-dot" style={{ backgroundColor: color }} />
<span className="popup-title">{title}</span>
<button className="popup-close" onClick={onClose}>×</button>
</div>
<div className="popup-body">{body}</div>
</div>
);
}
function App() {
const [insight, setInsight] = useState<Insight | null>(() => {
try { return tb.insight<Insight>(); } catch { return null; }
});
const [isGenerating, setIsGenerating] = useState(false);
const [tooltip, setTooltip] = useState<TooltipState>({ visible: false, x: 0, y: 0, content: null });
const [sortBy, setSortBy] = useState<DimKey | "total" | "name">("total");
const [sortAsc, setSortAsc] = useState(false);
const [expanded, setExpanded] = useState<Set<string>>(new Set());
const toggleExpand = (job: string) => {
setExpanded(prev => {
const next = new Set(prev);
if (next.has(job)) next.delete(job);
else next.add(job);
return next;
});
};
const handleAnalyze = async () => {
setIsGenerating(true);
try {
const result = await tb.infer<Insight>();
if (result) {
setInsight(result);
setTooltip(prev => ({ ...prev, visible: false }));
}
} finally {
setIsGenerating(false);
}
};
const triggerPopup = (e: React.MouseEvent, content: TooltipContent) => {
e.stopPropagation();
const popupWidth = 320;
const popupHeight = 240;
const padding = 15;
const edgeMargin = 10;
const vw = window.innerWidth;
const vh = window.innerHeight;
let x = e.clientX + padding;
let y = e.clientY + padding;
if (x + popupWidth > vw - edgeMargin) x = e.clientX - popupWidth - padding;
if (y + popupHeight > vh - edgeMargin) y = e.clientY - popupHeight - padding;
x = Math.max(edgeMargin, Math.min(x, vw - popupWidth - edgeMargin));
y = Math.max(edgeMargin, Math.min(y, vh - popupHeight - edgeMargin));
setTooltip({ visible: true, x, y, content });
};
const sortedJobs = insight?.jobs.slice().sort((a, b) => {
let cmp = 0;
if (sortBy === "name") {
cmp = a.job.localeCompare(b.job);
} else if (sortBy === "total") {
cmp = totalMessiness(b.scores) - totalMessiness(a.scores);
} else {
cmp = (b.scores[sortBy] || 0) - (a.scores[sortBy] || 0);
}
return sortAsc ? -cmp : cmp;
});
const handleSort = (key: DimKey | "total" | "name") => {
if (sortBy === key) {
setSortAsc(!sortAsc);
} else {
setSortBy(key);
setSortAsc(false);
}
};
return (
<div className="app" onClick={() => setTooltip(prev => ({ ...prev, visible: false }))}>
<Popup state={tooltip} onClose={() => setTooltip(prev => ({ ...prev, visible: false }))} />
<header>
<h1>How Automatable Is Your Job?</h1>
<p className="brand">The Messiness Index</p>
<p className="tagline">Higher messiness = harder to automate</p>
<button className={`analyze-btn ${isGenerating ? "loading" : ""}`} onClick={handleAnalyze}>
{isGenerating ? "Analyzing..." : "Analyze Jobs"}
</button>
</header>
{insight && (
<>
<div className="mobile-sort">
<label>Sort by</label>
<select
value={sortBy}
onChange={e => {
setSortBy(e.target.value as any);
setSortAsc(false);
}}
>
<option value="total">Total</option>
<option value="name">Name</option>
{DIMENSIONS.map(d => (
<option key={d.key} value={d.key}>{d.full}</option>
))}
</select>
<button className="sort-dir-btn" onClick={() => setSortAsc(!sortAsc)}>
{sortAsc ? "↑" : "↓"}
</button>
</div>
<div className="card-list">
{sortedJobs?.map(job => {
const total = totalMessiness(job.scores);
const isOpen = expanded.has(job.job);
return (
<div key={job.job} className={`card ${isOpen ? "open" : ""}`}>
<button className="card-header" onClick={() => toggleExpand(job.job)}>
<span className="card-job">{job.job}</span>
<span className="card-right">
<span className="card-total">{total}</span>
<span className={`card-chevron ${isOpen ? "open" : ""}`}>▾</span>
</span>
</button>
{isOpen && (
<div className="card-body">
<div className="card-scores">
{DIMENSIONS.map(d => {
const score = job.scores[d.key] ?? 0;
return (
<div key={d.key} className="card-score-row">
<span className="card-dim-label">{d.full}</span>
<span className="card-score-badge" style={{ background: scoreColor(score) }}>
{SCORE_LABELS[score]}
</span>
</div>
);
})}
</div>
{job.notes && <p className="card-notes">{job.notes}</p>}
</div>
)}
</div>
);
})}
</div>
<div className="table-wrap">
<table className="matrix">
<thead>
<tr>
<th className={`sortable ${sortBy === "name" ? "active" : ""}`} onClick={() => handleSort("name")}>
<div className="header-content">
<span className="header-spacer" />
<span className="dim-label">Job</span>
<div className="header-icons">
<span className="dim-info-spacer" />
<span className="sort-arrow-wrap">{sortBy === "name" && (sortAsc ? "↑" : "↓")}</span>
</div>
</div>
</th>
{DIMENSIONS.map(d => (
<th
key={d.key}
className={`sortable dim-header ${sortBy === d.key ? "active" : ""}`}
onClick={() => handleSort(d.key)}
>
<div className="header-content">
<span className="header-spacer" />
<span className="dim-label">{d.full}</span>
<div className="header-icons">
<button
className="dim-info-trigger"
onClick={(e) => triggerPopup(e, {
title: d.full,
body: <p>{d.desc}</p>,
color: "var(--accent)"
})}
>?</button>
<span className="sort-arrow-wrap">{sortBy === d.key && (sortAsc ? "↑" : "↓")}</span>
</div>
</div>
</th>
))}
<th
className={`sortable ${sortBy === "total" ? "active" : ""}`}
onClick={() => handleSort("total")}
>
<div className="header-content">
<span className="header-spacer" />
<span className="dim-label">Total</span>
<div className="header-icons">
<span className="dim-info-spacer" />
<span className="sort-arrow-wrap">{sortBy === "total" && (sortAsc ? "↑" : "↓")}</span>
</div>
</div>
</th>
</tr>
</thead>
<tbody>
{sortedJobs?.map(job => (
<tr key={job.job}>
<td
className="job-name clickable"
onClick={(e) => triggerPopup(e, {
title: job.job,
body: (
<div className="job-popup-content">
<p className="popup-notes">{job.notes}</p>
<div className="radar-wrap">
<RadarChart scores={job.scores} />
</div>
</div>
),
color: "var(--accent)"
})}
>
{job.job}
</td>
{DIMENSIONS.map(d => {
const score = job.scores[d.key] ?? 0;
return (
<td key={d.key} className="score-cell" style={{ background: scoreColor(score) }}>
{SCORE_LABELS[score]}
</td>
);
})}
<td className="total-cell">{totalMessiness(job.scores)}</td>
</tr>
))}
</tbody>
</table>
</div>
</>
)}
{!insight && (
<div className="empty">
<p>Add any jobs to the Data tab, then click "Analyze Jobs".</p>
<p className="hint">We'll score each one across 6 dimensions of task complexity.</p>
</div>
)}
</div>
);
}
function RadarChart({ scores }: { scores: JobAnalysis["scores"] }) {
const size = 200;
const cx = size / 2;
const cy = size / 2;
const r = 70;
const dims = DIMENSIONS.filter(d => scores[d.key] > 0);
if (dims.length < 3) return <div className="radar-empty">Not enough dimensions for radar</div>;
const points = dims.map((d, i) => {
const angle = (Math.PI * 2 * i) / dims.length - Math.PI / 2;
const val = scores[d.key] / 5;
return {
x: cx + Math.cos(angle) * r * val,
y: cy + Math.sin(angle) * r * val,
lx: cx + Math.cos(angle) * (r + 20),
ly: cy + Math.sin(angle) * (r + 20),
label: d.label,
};
});
const pathD = points.map((p, i) => `${i === 0 ? "M" : "L"} ${p.x} ${p.y}`).join(" ") + " Z";
const gridLevels = [0.2, 0.4, 0.6, 0.8, 1];
return (
<svg width={size} height={size} className="radar">
{gridLevels.map(level => {
const gridPath = dims.map((_, i) => {
const angle = (Math.PI * 2 * i) / dims.length - Math.PI / 2;
const x = cx + Math.cos(angle) * r * level;
const y = cy + Math.sin(angle) * r * level;
return `${i === 0 ? "M" : "L"} ${x} ${y}`;
}).join(" ") + " Z";
return <path key={level} d={gridPath} className="radar-grid" />;
})}
{dims.map((_, i) => {
const angle = (Math.PI * 2 * i) / dims.length - Math.PI / 2;
return (
<line
key={i}
x1={cx}
y1={cy}
x2={cx + Math.cos(angle) * r}
y2={cy + Math.sin(angle) * r}
className="radar-axis"
/>
);
})}
<path d={pathD} className="radar-area" />
{points.map((p, i) => (
<text key={i} x={p.lx} y={p.ly} className="radar-label" textAnchor="middle" dominantBaseline="middle">
{p.label}
</text>
))}
</svg>
);
}
createRoot(document.getElementById("root")!).render(<App />);
```
**styles.css**
```css
:root {
--bg: #0d1117;
--panel: #161b22;
--border: #30363d;
--text: #c9d1d9;
--dim: #8b949e;
--accent: #58a6ff;
--shadow: rgba(0, 0, 0, 0.4);
}
html[data-theme="light"] {
--bg: #f6f8fa;
--panel: #ffffff;
--border: #d0d7de;
--text: #24292f;
--dim: #57606a;
--accent: #0969da;
--shadow: rgba(0, 0, 0, 0.1);
}
* { box-sizing: border-box; margin: 0; padding: 0; }
html, body { height: 100%; }
body {
font: 13px/1.5 -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, sans-serif;
background: var(--bg);
color: var(--text);
padding: 16px;
}
.app {
max-width: 900px;
margin: 0 auto;
padding-bottom: 16px;
}
header { text-align: center; margin-bottom: 24px; }
h1 { font-size: 32px; font-weight: 600; margin-bottom: 8px; }
.brand { font-size: 24px; color: var(--accent); font-weight: 500; margin-bottom: 8px; }
.tagline { color: var(--dim); font-size: 18px; margin-bottom: 20px; }
.analyze-btn {
background: var(--accent);
color: #fff;
border: none;
padding: 10px 24px;
border-radius: 6px;
font: inherit;
font-weight: 500;
cursor: pointer;
transition: opacity 0.2s;
}
.analyze-btn:hover { opacity: 0.9; }
.analyze-btn.loading { opacity: 0.6; cursor: wait; }
.summary {
background: var(--panel);
border: 1px solid var(--border);
border-radius: 6px;
padding: 12px 16px;
margin-bottom: 16px;
font-size: 14px;
line-height: 1.6;
}
.table-wrap { overflow-x: auto; margin-bottom: 16px; }
.matrix {
width: 100%;
border-collapse: collapse;
background: var(--panel);
border: 1px solid var(--border);
border-radius: 6px;
overflow: hidden;
font-size: 12px;
}
.matrix th, .matrix td {
padding: 6px 8px;
text-align: center;
border-bottom: 1px solid var(--border);
}
.matrix th {
background: var(--bg);
font-weight: 600;
font-size: 11px;
text-transform: uppercase;
letter-spacing: 0.5px;
padding: 0;
}
.header-content {
display: grid;
grid-template-columns: 1fr auto 1fr;
align-items: center;
padding: 8px 4px;
}
.header-spacer, .header-icons {
display: flex;
align-items: center;
}
.header-icons {
gap: 2px;
justify-content: flex-end;
}
.matrix th.sortable { cursor: pointer; user-select: none; }
.matrix th.sortable:hover { color: var(--accent); }
.matrix th.active { color: var(--accent); }
.dim-header { position: relative; }
.dim-label { cursor: pointer; flex: 1; text-align: center; white-space: nowrap; }
.dim-info-trigger {
background: var(--border);
color: var(--dim);
border: none;
border-radius: 50%;
width: 14px;
height: 14px;
font-size: 10px;
line-height: 14px;
cursor: pointer;
display: inline-block;
vertical-align: middle;
flex-shrink: 0;
}
.dim-info-trigger:hover { background: var(--accent); color: white; }
.dim-info-spacer {
width: 14px;
height: 14px;
flex-shrink: 0;
}
.sort-arrow-wrap {
width: 10px;
text-align: center;
font-size: 11px;
color: var(--accent);
flex-shrink: 0;
font-family: monospace;
}
.matrix tbody tr:hover { background: rgba(88, 166, 255, 0.1); }
.job-name { text-align: left; font-weight: 500; white-space: nowrap; }
.job-name.clickable { cursor: pointer; color: var(--accent); text-decoration: underline; text-decoration-color: transparent; transition: text-decoration-color 0.2s; }
.job-name.clickable:hover { text-decoration-color: var(--accent); }
.score-cell { color: #fff; font-weight: 500; }
.total-cell { font-weight: 600; background: var(--bg); }
/* Popup Styles */
.popup-container {
position: fixed;
z-index: 1000;
background: var(--panel);
border: 2px solid;
border-radius: 12px;
padding: 16px;
width: 320px;
box-shadow: 0 10px 25px -5px var(--shadow);
animation: popupFade 0.15s ease-out;
}
@keyframes popupFade {
from { opacity: 0; transform: translateY(5px); }
to { opacity: 1; transform: translateY(0); }
}
.popup-header {
display: flex;
align-items: center;
gap: 8px;
margin-bottom: 12px;
border-bottom: 1px solid var(--border);
padding-bottom: 8px;
}
.popup-dot { width: 8px; height: 8px; border-radius: 50%; }
.popup-title { font-weight: 700; font-size: 15px; flex: 1; }
.popup-close { background: none; border: none; color: var(--dim); font-size: 20px; cursor: pointer; line-height: 1; }
.popup-close:hover { color: var(--text); }
.popup-body { font-size: 13px; line-height: 1.4; }
.popup-notes { color: var(--dim); font-style: italic; margin-bottom: 12px; }
.job-popup-content { display: flex; flex-direction: column; align-items: center; }
.radar-wrap { display: flex; justify-content: center; }
.radar-empty { color: var(--dim); font-style: italic; }
.radar .radar-grid { fill: none; stroke: var(--border); stroke-width: 1; }
.radar .radar-axis { stroke: var(--border); stroke-width: 1; }
.radar .radar-area { fill: rgba(88, 166, 255, 0.3); stroke: var(--accent); stroke-width: 2; }
.radar .radar-label { fill: var(--dim); font-size: 10px; }
.empty {
text-align: center;
padding: 48px;
color: var(--dim);
}
.empty .hint { margin-top: 8px; font-size: 12px; }
/* Mobile card view */
.mobile-sort { display: none; }
.card-list { display: none; }
@media (max-width: 600px) {
h1 { font-size: 22px; }
.brand { font-size: 18px; }
.tagline { font-size: 14px; }
.table-wrap { display: none; }
.mobile-sort { display: flex; align-items: center; gap: 8px; margin-bottom: 12px; }
.mobile-sort label { font-size: 12px; color: var(--dim); text-transform: uppercase; letter-spacing: 0.5px; font-weight: 600; }
.mobile-sort select {
flex: 1;
background: var(--panel);
color: var(--text);
border: 1px solid var(--border);
border-radius: 6px;
padding: 6px 8px;
font: inherit;
}
.sort-dir-btn {
background: var(--panel);
color: var(--accent);
border: 1px solid var(--border);
border-radius: 6px;
width: 32px;
height: 32px;
font-size: 16px;
cursor: pointer;
font-family: monospace;
}
.card-list { display: flex; flex-direction: column; gap: 1px; background: var(--border); border: 1px solid var(--border); border-radius: 6px; overflow: hidden; }
.card { background: var(--panel); }
.card-header {
display: flex;
align-items: center;
justify-content: space-between;
width: 100%;
padding: 10px 12px;
background: none;
border: none;
color: var(--text);
font: inherit;
font-weight: 500;
cursor: pointer;
text-align: left;
}
.card-header:active { background: rgba(88, 166, 255, 0.08); }
.card-right { display: flex; align-items: center; gap: 8px; }
.card-total { font-weight: 700; font-size: 15px; color: var(--accent); min-width: 24px; text-align: right; }
.card-chevron { font-size: 10px; color: var(--dim); transition: transform 0.15s; }
.card-chevron.open { transform: rotate(180deg); }
.card-body { padding: 0 12px 12px; }
.card-scores { display: flex; flex-direction: column; gap: 4px; }
.card-score-row { display: flex; align-items: center; justify-content: space-between; }
.card-dim-label { font-size: 12px; color: var(--dim); }
.card-score-badge {
font-size: 11px;
font-weight: 600;
color: #fff;
padding: 1px 8px;
border-radius: 4px;
min-width: 48px;
text-align: center;
}
.card-notes { margin-top: 8px; font-size: 12px; color: var(--dim); font-style: italic; line-height: 1.4; }
}
```
**index.html**
```html
<div id="root"></div>
```
**data.txt**
```txt
Nurse
Programmer
Quant (finance)
Plumber
Litigation lawyer
ER Doctor
Factory assembly worker
Teacher (K-12)
CEO
Security researcher
Radiologist
Graphic designer
Therapist
Truck driver
Journalist
Real estate agent
Paralegal
Cashier
```
**infer.md**
```md
Analyze each job in the input data using the Task Messiness Framework.
## The Six Dimensions
Score each dimension from 0-5:
- **0 = N/A** - Dimension doesn't apply
- **1 = Very Low** - Negligible factor
- **2 = Low** - Minor factor
- **3 = Medium** - Meaningful factor
- **4 = High** - Major factor
- **5 = Very High** - Dominant factor
### Dimensions:
1. **Coordination** (people × synchronization × time pressure × relationship depth)
- How many people involved? How tightly synchronized? Real-time pressure? Deep relationships needed?
2. **Knowledge** (domains × depth)
- How many domains? How deep in each?
3. **Exceptions** (frequency × cost × novelty)
- How often do things go wrong? How costly? How unprecedented?
4. **Feedback** (measurability × speed × agreement)
- How clear is success? How fast do you know? Do stakeholders agree?
5. **Environment** (variability × accessibility × precision)
- Physical space unpredictability? Access constraints? Dexterity required?
- Score 0 for pure knowledge work with no physical component.
6. **Adversarial** (active opposition × stakes × adaptiveness)
- Is someone actively working against you? High stakes? Do they adapt?
- Score 0 if no adversarial dynamics.
## Output Format
Return a JSON object with:
- `jobs`: Array of job analyses, each with `job`, `scores` (object with all 6 dimension keys), and `notes` (1-2 sentence rationale)
```
**insight.json**
```json
{
"jobs": [
{
"job": "Nurse",
"scores": { "coordination": 5, "knowledge": 4, "exceptions": 5, "feedback": 4, "environment": 4, "adversarial": 1 },
"notes": "Extreme coordination with doctors, patients, families. Every patient is different. Physical care on variable human bodies. Patients may resist but aren't adversarial."
},
{
"job": "Programmer",
"scores": { "coordination": 4, "knowledge": 3, "exceptions": 4, "feedback": 3, "environment": 0, "adversarial": 1 },
"notes": "Works with PMs, designers, other devs. Multiple domains at moderate depth. Bugs and edge cases are frequent. Feedback is mixed (fast for correctness, slow for quality)."
},
{
"job": "Quant (finance)",
"scores": { "coordination": 3, "knowledge": 4, "exceptions": 4, "feedback": 3, "environment": 0, "adversarial": 4 },
"notes": "Deep multi-domain expertise. Markets are adversarial - others exploit your edge. P&L is measurable but attribution (skill vs luck) is contested."
},
{
"job": "Plumber",
"scores": { "coordination": 2, "knowledge": 3, "exceptions": 4, "feedback": 2, "environment": 4, "adversarial": 0 },
"notes": "Often solo. Every building is different with hidden conditions. High environmental variability - crawl spaces, behind walls. No adversarial dynamics."
},
{
"job": "Litigation lawyer",
"scores": { "coordination": 5, "knowledge": 4, "exceptions": 4, "feedback": 2, "environment": 1, "adversarial": 5 },
"notes": "Coordinates with courts, clients, experts. Opposing counsel is explicitly adversarial and adapts to your strategy. Outcomes take years."
},
{
"job": "ER Doctor",
"scores": { "coordination": 5, "knowledge": 5, "exceptions": 5, "feedback": 3, "environment": 4, "adversarial": 1 },
"notes": "Extreme time pressure, coordination with entire hospital. Deep expertise across emergencies. Physical intervention on critical patients. Life-or-death stakes."
},
{
"job": "Factory assembly worker",
"scores": { "coordination": 2, "knowledge": 2, "exceptions": 2, "feedback": 1, "environment": 2, "adversarial": 0 },
"notes": "Structured, repetitive tasks. Clear success criteria. Controlled environment. Already heavily automated."
},
{
"job": "Teacher (K-12)",
"scores": { "coordination": 4, "knowledge": 3, "exceptions": 4, "feedback": 2, "environment": 3, "adversarial": 2 },
"notes": "30 unique students, parents, administrators. Behavioral exceptions are constant. Outcomes take years to measure. Students may actively resist learning."
},
{
"job": "CEO",
"scores": { "coordination": 5, "knowledge": 4, "exceptions": 4, "feedback": 2, "environment": 1, "adversarial": 4 },
"notes": "All coordination, all the time. Competitors, activist investors, market forces are adversarial. Feedback is slow and contested by stakeholders."
},
{
"job": "Security researcher",
"scores": { "coordination": 2, "knowledge": 4, "exceptions": 4, "feedback": 3, "environment": 0, "adversarial": 5 },
"notes": "Deep technical expertise. Attackers actively evolve to bypass your defenses. The adversarial dimension is the core challenge - the problem resists being solved."
},
{
"job": "Radiologist",
"scores": { "coordination": 2, "knowledge": 4, "exceptions": 3, "feedback": 3, "environment": 0, "adversarial": 0 },
"notes": "Deep imaging expertise, but mostly independent reading. AI makes inroads on pattern recognition, but rare conditions, atypical presentations, and clinical context integration remain challenging."
},
{
"job": "Graphic designer",
"scores": { "coordination": 3, "knowledge": 3, "exceptions": 3, "feedback": 2, "environment": 0, "adversarial": 0 },
"notes": "Client coordination and iteration cycles. Design judgment and brand consistency. AI generates images but client relationships, revision loops, and taste remain human."
},
{
"job": "Therapist",
"scores": { "coordination": 3, "knowledge": 4, "exceptions": 4, "feedback": 1, "environment": 0, "adversarial": 1 },
"notes": "Deep relationship over months/years. Each patient unique. Feedback is extremely slow (therapeutic outcomes) and subjective. Patient resistance isn't adversarial but adds complexity."
},
{
"job": "Truck driver",
"scores": { "coordination": 2, "knowledge": 2, "exceptions": 3, "feedback": 2, "environment": 4, "adversarial": 0 },
"notes": "Self-driving's target, but environment is the barrier: weather, construction, rural roads, loading docks, mechanical issues. Highway driving is easier than last-mile."
},
{
"job": "Journalist",
"scores": { "coordination": 4, "knowledge": 3, "exceptions": 3, "feedback": 2, "environment": 2, "adversarial": 3 },
"notes": "Source relationships, editor coordination, deadline pressure. Sources may deceive, subjects may stonewall or sue. Quality is subjective; engagement metrics are gameable."
},
{
"job": "Real estate agent",
"scores": { "coordination": 4, "knowledge": 3, "exceptions": 3, "feedback": 2, "environment": 3, "adversarial": 2 },
"notes": "Coordinates buyers, sellers, lenders, inspectors. Local market knowledge. Physical property showings. Competing agents and negotiation dynamics. Zillow hasn't killed this yet."
},
{
"job": "Paralegal",
"scores": { "coordination": 3, "knowledge": 3, "exceptions": 2, "feedback": 2, "environment": 0, "adversarial": 1 },
"notes": "Supports attorneys with document prep and research. More routine than attorney work. AI document review is making inroads on the lower-complexity portions."
},
{
"job": "Cashier",
"scores": { "coordination": 1, "knowledge": 1, "exceptions": 2, "feedback": 1, "environment": 1, "adversarial": 0 },
"notes": "Minimal coordination, simple knowledge. Self-checkout has largely automated this. Remaining value is handling exceptions (returns, ID checks) and human presence preference."
}
]
}
```
**config.json**
```json
{
"dependencies": {
"react": "^19.2.0",
"react-dom": "^19.2.0"
},
"description": "How automatable is your job? The Messiness Index scores jobs across 6 dimensions of task complexity. Higher messiness = harder to automate.",
"inference": {
"title": "Analyze Jobs",
"dataTitle": "Jobs to Analyze",
"submitTitle": "Run Analysis"
}
}
```