Atlas · Methodology contrast
A 55.2 percentage-point gap between what chatbot-usage telemetry can observe and what economy-wide system-architecture observation actually finds — across 22 SOC major occupation groups.
The Anthropic Economic Index publishes three useful series: which occupations show up most in Claude.ai conversations, which tasks within those occupations get help, and how those patterns shift over time. It is the cleanest signal we have on how people consult AI through a chatbot interface.
That is not the same as how AI affects production. A radiologist who pastes scan summaries into Claude and asks for second opinions is in the dataset. A radiology workflow that runs contract-gated multi-model ensembles, ingests DICOM directly through agent orchestration, escalates ambiguous cases to a human reviewer, and produces a final report without a human ever opening a chat window — is not.
Massenkoff & McCrory (March 2026) formalize this gap. Below is their core decomposition extended with DaedArch's per-NAICS transformation corpus.
| Channel | What it captures | What it misses |
|---|---|---|
| Chatbot conversation (Anthropic Index) | User-initiated AI consultation patterns | Anything not initiated as a chat turn |
| Contract-gated execution | Tool calls under formal preconditions | Conversational coaching |
| Agent orchestration | Multi-step automated workflows | Single-shot human judgment |
| Visual scanning & structured pipelines | Continuous machine-led inspection | Anything that's still a person reading a screen |
The Anthropic Index sees channel 1. The Atlas adds channels 2–4. The gap between them is the 55.2 pp number above — and it is not evenly distributed.
Across 22 SOC major occupation groups, blind-spot magnitude varies sharply. The table below is the headline contrast (full per-SOC table available via daedarch.business.atlas_intelligence_v1 on any NAICS code):
| SOC major group | Anthropic observed | Atlas reality | Blind-spot gap |
|---|---|---|---|
| 15 — Computer & Mathematical | 62.4% | 71.2% | 8.8 pp |
| 13 — Business & Financial Operations | 19.1% | 63.7% | 44.6 pp |
| 11 — Management | 12.8% | 58.4% | 45.6 pp |
| 43 — Office & Administrative Support | 8.2% | 76.9% | 68.7 pp |
| 49 — Installation, Maintenance, Repair | 3.1% | 29.4% | 26.3 pp |
| 53 — Transportation & Material Moving | 1.8% | 71.6% | 69.8 pp |
Source: Massenkoff & McCrory (March 2026), Atlas SOC × NAICS corpus (atlas_transformations, n=23,797).
The pattern is consistent: occupations whose AI exposure routes through chatbots (computing, knowledge work) show small gaps. Occupations whose AI exposure routes through automated structured pipelines — back-office processing, transportation, logistics, maintenance — show very large gaps because the work happens without anyone typing a question.
If a methodology only measures conversations, it cannot see the categories of work that are being automated away from conversations. The Atlas exists to fix that omission.
Trellison rates methodology, not conclusions. Two consequences:
The Atlas is queryable at the per-NAICS level via daedarch.business.atlas_intelligence_v1 (see the operational tool on DaedArch). Inputs: NAICS code; outputs: SOC × NAICS transformations, scenario contrast (chatbot view vs. system-architecture reality), impact metrics, tools contrast, industry packages.
Source corpus: atlas_transformations (23,797 rows), atlas_oews (82,522 rows), Massenkoff & McCrory (March 2026). Methodology disclosure and reproduction instructions: /atlas/methodology.