Atlas · Transformation corpus
23,797 industry × occupation transformations, decomposed
Per-NAICS, per-SOC: before scenario, after scenario, daedarch pitch, generic-AI counterfactual, pain points, tools, financial shift, human role remaining. The corpus that backs every Atlas claim.
Top NAICS codes by transformation density
| NAICS | Industry | Transformations | Avg wage | Avg human-hours/wk |
|---|
| 999300 | Unclassified | 557 | $64,881 | 0 |
| 561300 | Employment services | 455 | $58,074 | 0 |
| 611300 | Colleges, universities, and professional schools | 454 | $67,030 | 0 |
| 551100 | Management of companies and enterprises | 419 | $84,511 | 0 |
| 999200 | Unclassified | 405 | $64,776 | 0 |
| 999100 | Unclassified | 390 | $89,571 | 0 |
| 622100 | General medical and surgical hospitals | 376 | $71,790 | 0 |
| 541600 | Management, scientific, and technical consulting services | 317 | $76,045 | 0 |
| 611100 | Elementary and secondary schools | 315 | $59,029 | 0 |
| 541300 | Architectural, engineering, and related services | 280 | $84,170 | 0 |
| 4230A1 | Merchant wholesalers, durable goods | 249 | $66,123 | 0 |
| 541700 | Scientific research and development services | 247 | $107,019 | 0 |
One row, fully decomposed
Below is a single transformation row for NAICS 561300 (Employment services) × SOC 53-0000 (Transportation and Material Moving). This is the shape every row takes.
Before scenario
Maria arrives at 5 AM as the dispatch supervisor for a 40-vehicle regional carrier. She pulls up three separate systems: the TMS, the driver communication app, and the customer portal. Overnight, 47 new pickup requests came in via email, phone voicemail, and the website. She manually transcribes each into the TMS, cross-checking addresses against the customer database. By 6:30 AM, she's still entering data. At 7 AM, drivers start calling asking for their routes—she prints manifests from the TMS, but three are missing special instructions because customers called after the system cutoff. She ve…
After scenario (Atlas reality, with daedarch)
Maria arrives at 5 AM to find the overnight work already done. The Manifest Harvester ingested all 47 requests automatically—emails parsed via MCP connector, phone voicemails transcribed via IVR harvester, website submissions synced via API. Each request is already in the TMS with addresses validated against the customer database and special instructions extracted from email threads. The Dr. Strange GTM engine ran 10K route simulations overnight and assigned loads to the 40 vehicles based on real-time traffic predictions, driver HOS status (harvested from ELD systems), vehicle capacity, and fu…
Generic AI counterfactual (Anthropic-Index-style)
Maria still arrives at 5 AM, but now she uses a Claude-powered chatbot to help summarize overnight emails and extract key details. The bot reads 30 of the 47 requests and suggests pickup times and addresses—she still manually verifies and enters them into the TMS, which saves maybe 20 minutes. A generic route optimization dashboard shows her yesterday's performance metrics and suggests some route improvements, but she doesn't trust the recommendations because they don't account for driver preferences or real-time traffic. She still prints manifests and briefs drivers verbally, though the chatb…
Source: atlas_transformations._id=69a86889eb07a68e850a37d0 · methodology: anthropic_vs_daedarch_v2
Why decompose this way
A claim like "AI will automate X% of jobs" is unfalsifiable in this form — the percentage depends entirely on which methodology you used. The Atlas publishes the four scenarios per row (before, with-AI-only, with-daedarch, after) so any reader can replicate the underlying model, vary an assumption, and see what changes. The disclosure is the methodology.