S3319-119

In Committee

Workforce of the Future Act of 2025

119th Congress Introduced Dec 3, 2025

Analysis under review: This bill has generated analysis that may be too generic or incomplete. Clause-level evidence remains available below.

Summary

What This Bill Does

Requires interagency reporting on artificial intelligence and the workforce, funds education and workforce grants in emerging and advanced technology, and adds related data collection to federal education research.

Who Benefits and How

Students, workers, educational institutions, labor organizations, and training providers gain new federal grant opportunities and a more developed federal evidence base on AI's workforce effects.

Who Bears the Burden and How

Labor, Commerce, and Education agencies must prepare recurring reports, administer grants, collect performance data, and expand federal research activities.

Key Provisions

  • Requires interagency interim, final, and updated reports on AI's impact on the U.S. workforce.
  • Creates Department of Education grants for emerging and advanced technology education and Department of Labor grants for workforce training tied to AI disruption.
  • Imposes recurring reporting on grantees and adds emerging-technology education measures to federal education data collection.

Evidence Chain:

This summary is generated from the full bill text using AI analysis. Expand "Detailed Analysis" below for identified beneficiaries/burden bearers.

At a Glance

What This Bill Does

Requires interagency reporting on artificial intelligence and the workforce, funds education and workforce grants in emerging and advanced technology, and adds related data collection to federal education research.

Key Policy Areas

Labor, Technology, Education, Government Operations

Primary Purpose

Requires interagency reporting on artificial intelligence and the workforce, funds education and workforce grants in emerging and advanced technology, and adds related data collection to federal education research.

Policy Domains

Labor Technology Education Government Operations

Main Provisions

Identified Gains
Contextual inference, no direct clause citation
  • Students and workers needing emerging-technology skills
  • Educational institutions and labor organizations
Model: codex-gpt-5 | Version: bill_summary_v2 | Source: is

Contextual inference, no direct clause citation

Identified Costs
Contextual inference, no direct clause citation
  • Labor, Commerce, and Education administrators
  • Grant recipients subject to reporting requirements
Model: codex-gpt-5 | Version: bill_summary_v2 | Source: is

Contextual inference, no direct clause citation

Legislative Progress

In Committee
Introduced Committee Passed
Dec 3, 2025

Ms. Blunt Rochester (for herself, Ms. Hirono, and Mr. Schiff) …

Dec 3, 2025

Read twice and referred to the Committee on Health, Education, …

Dec 3, 2025

Introduced in Senate

Stakeholder Effects

cui bono?

How this legislation distributes effects. Mention counts reflect frequency, not effect magnitude.

Government
5 mentions across 5 clauses
-5 negative

Department of Education grant administrators, Department of Labor grant administrators, Education and labor grant recipients

Labor
3 mentions across 2 clauses
+3 positive

Labor organizations and other eligible workforce-training entities, Workers most impacted by artificial intelligence, Workers, educators, and workforce policymakers

Education
3 mentions across 2 clauses
+3 positive

Education policymakers and researchers, Schools, colleges, and other eligible education entities, Students gaining access to emerging and advanced technology education

9/11
sections analyzed
Full impact breakdown

Bill Structure & Actor Mappings

Who is "The Secretary" in each section?

Domains
Labor Technology Education Government Operations
Actor Mappings
"the secretary of labor"
→ Secretary of Labor
"the secretary of commerce"
→ Secretary of Commerce
"the secretary of education"
→ Secretary of Education

We use a combination of our own taxonomy and classification in addition to large language models to assess meaning and potential beneficiaries. High confidence means strong textual evidence. Always verify with the original bill text.

Learn more about our methodology