बीमांकिक
व्यापार और वित्तAI एक्सपोजर
- डेटा स्रोत: BLSप्रकाशन: 2026-08
बहुत उच्च· सापेक्ष
कमचार सापेक्ष श्रेणियाँबहुत उच्चव्यवसाय-समूह स्तर का मान
मापक्रम, आधार और स्रोत
चार सापेक्ष बैंड (कम / मध्यम / उच्च / बहुत उच्च)
BLS रोज़गार पूर्वानुमान तालिका के 831 विस्तृत व्यवसायों के आधार पर। मान NEM (नेशनल एम्प्लॉयमेंट मैट्रिक्स) कोड के स्तर पर दिया जाता है, इसलिए एक ही NEM कोड वाले व्यवसायों को वही बैंड मिलता है
- डेटा स्रोत: Anthropicप्रकाशन: 2026-03
0.054
0.000यहाँ दिए गए मानों की सीमा0.745मापक्रम, आधार और स्रोत
- डेटा स्रोत: ILOप्रकाशन: 2025
0.56
0.09यहाँ दिए गए मानों की सीमा0.70व्यवसाय-समूह स्तर का मान
मापक्रम, आधार और स्रोत
जेनरेटिव AI एक्सपोजर सूचकांक, प्रकाशित रूप में 0–1
ISCO-08 यूनिट समूह — समान कोड वाले सभी व्यवसायों को यही मान मिलता है
यह इस साइट की गणना है, ILO द्वारा प्रकाशित आँकड़ा नहीं: इस साइट द्वारा ILO डेटासेट से जोड़े गए 1,012 व्यवसायों में से 8% इस मान के बराबर या उससे अधिक हैं।
यह स्रोत किस प्रकार का आँकड़ा प्रकाशित करता है
BLS की श्रेणी सापेक्ष रैंक है, निरपेक्ष स्तर नहीं, और यह प्रथम-हस्त माप भी नहीं है: यह कई प्रकाशित अध्ययनों में व्यवसाय की पर्सेंटाइल रैंकों को चार बैंडों में बांटती है। यह रोज़गार या वेतन का पूर्वानुमान नहीं है, न अपनाए जाने की संभावना, और यह स्वचालन और संवर्धन में अंतर नहीं करती।
Task-level exposure
केवल एक्सपोज़र वाले कार्य| Task | Claude.aiRaw / share % | APIRaw / share % |
|---|---|---|
Provide advice to clients on a contract basis, working as a consultant.15-2011 | 0.251595.4 | 0.0138100.0 |
Determine or help determine company policy, and explain complex technical matters to company executives, government officials, shareholders, policyholders, or the public.15-2011 | 0.00451.7 | —0 |
Determine policy contract provisions for each type of insurance.15-2011 | 0.00301.1 | —0 |
Provide expertise to help financial institutions manage risks and maximize returns associated with investment products or credit offerings.15-2011 | 0.00281.1 | —0 |
Design, review and help administer insurance, annuity and pension plans, determining financial soundness and calculating premiums.15-2011 | 0.00180.7 | —0 |
| Not observed on any surface — 9 task(s) — These tasks have no row in the source for this release. The 0 in the share row is a display-stage composition ratio; absence is what the — in the raw row states. | ||
Ascertain premium rates required and cash reserves and liabilities necessary to ensure payment of future benefits. | —0 | —0 |
Analyze statistical information to estimate mortality, accident, sickness, disability, and retirement rates. | —0 | —0 |
Collaborate with programmers, underwriters, accounts, claims experts, and senior management to help companies develop plans for new lines of business or improvements to existing business. | —0 | —0 |
Testify before public agencies on proposed legislation affecting businesses. | —0 | —0 |
Testify in court as expert witness or to provide legal evidence on matters such as the value of potential lifetime earnings of a person who is disabled or killed in an accident. | —0 | —0 |
Construct probability tables for events such as fires, natural disasters, and unemployment, based on analysis of statistical data and other pertinent information. | —0 | —0 |
Determine equitable basis for distributing surplus earnings under participating insurance and annuity contracts in mutual companies. | —0 | —0 |
Manage credit and help price corporate security offerings. | —0 | —0 |
Explain changes in contract provisions to customers. | —0 | —0 |
Values in this tab are predicted labels, not observations. Eloundou et al. (2023) published two rating regimes — human raters and GPT-4 — and the β shown here is derived from the GPT-4 rater basis alone; the same task can take a different value under the other regime. The unit and the meaning differ from the observed shares (%) in the other tabs, so do not place them on the same axis.
| Task | βE1 + 0.5 × E2 |
|---|---|
Ascertain premium rates required and cash reserves and liabilities necessary to ensure payment of future benefits.O*NET Task ID 3500 | 0.5 |
Analyze statistical information to estimate mortality, accident, sickness, disability, and retirement rates.O*NET Task ID 3501 | 0.5 |
Design, review, and help administer insurance, annuity and pension plans, determining financial soundness and calculating premiums.O*NET Task ID 3502 | 0.5 |
Collaborate with programmers, underwriters, accounts, claims experts, and senior management to help companies develop plans for new lines of business or improvements to existing business.O*NET Task ID 3503 | 0.5 |
Determine, or help determine, company policy, and explain complex technical matters to company executives, government officials, shareholders, policyholders, or the public.O*NET Task ID 3504 | 0.5 |
Testify before public agencies on proposed legislation affecting businesses.O*NET Task ID 3505 | 0.5 |
Provide advice to clients on a contract basis, working as a consultant.O*NET Task ID 3506 | 0.5 |
Testify in court as expert witness or to provide legal evidence on matters such as the value of potential lifetime earnings of a person disabled or killed in an accident.O*NET Task ID 3507 | 0.5 |
Construct probability tables for events such as fires, natural disasters, and unemployment, based on analysis of statistical data and other pertinent information.O*NET Task ID 3508 | 0.5 |
Determine policy contract provisions for each type of insurance.O*NET Task ID 3509 | 0.5 |
β = E1 + 0.5 × E2 · E1 = tasks where direct LLM access alone cuts time by at least 50%, E2 = tasks where software built on top of an LLM cuts time by at least 50%. Values take only 0 / 0.5 / 1.0.
Data sources & licenses — O*NET®, Anthropic Economic Index, Eloundou et al. (2023): see full notices on the Credits page
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मार्च 2026: Evergreen blog post publish. 63% exposure, 49% risk, 82% premium automation ke bawajud BLS +23% growth.
[स्रोत: AI Changing Work Blog]