America's AI Action Plan
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Winning the Race America's AI Action Plan

A coordinated Federal effort would be beneficial in establishing a dynamic, “try-first” culture for AI across American industry

Establish regulatory sandboxes or AI Centers of Excellence around the country where researchers, startups, and established enterprises can rapidly deploy and test AI tools while committing to open sharing of data and results.

Accelerate the development and adoption of national standards for AI systems and to measure how much AI increases productivity at realistic tasks in those domains.

Promoting the integration of AI skill development into relevant programs, including career and technical education (CTE), workforce training, apprenticeships, and other federally supported skills initiatives.

Offer tax-free reimbursement for AI-related training and help scale private-sector investment in AI skill development, preserving jobs for American workers.

Led by the Bureau of Labor Statistics (BLS) and DOC through the Census Bureau and the Bureau of Economic Analysis (BEA), study AI’s impact on the labor market by using data they already collect on these topics, such as the firm-level AI adoption trends the Census Bureau tracks in its Business Trends and Outlook Survey. These agencies could then provide analysis of AI adoption, job creation, displacement, and wage effects.

U.S. AI Workforce Labor Market Dynamics

The U.S. AI Workforce: Analyzing Current Supply and Growth

Future of Work with AI Agents

Explore the Future of Your Work with AI Agents

Research The effects of AI on firms and workers

Incorporating AI impacts in BLS employment projections: occupational case studies

Superagency in the workplace: Empowering people to unlock AI’s full potential

Assessing the Implementation of Federal AI Leadership and Compliance Mandates

🧪 Evaluation Methodology for Humanity’s Last Exam (HLE) is designed to rigorously test the reasoning capabilities of AI models—far beyond simple fact recall or pattern matching. Here's how it works:

🔍 Core Evaluation Principles

Closed-ended questions: Each item has a definitive answer—either multiple choice or exact-match short answer. No partial credit, no fuzzy interpretation.

Google-proof design: Answers aren’t searchable online, so models must reason, not retrieve

Held-out test set: A private subset of questions is reserved to detect overfitting and ensure generalization

🧠 What’s Being Measured

True reasoning ability: Can the model deduce, infer, and synthesize knowledge across disciplines?

Cross-domain competence: Questions span over 100 subjects, from advanced mathematics to ancient linguistics

Human-level comparison: Graduate students average ~90% accuracy, while top AI models struggle to reach 30%

🧰 Evaluation Process

Models are tested independently by Artificial Analysis, using standardized prompts and answer formats.

Each response is scored as correct or incorrect—no room for ambiguity.

Performance is tracked on a public leaderboard, showcasing how frontier models stack up against one another

💡 Why It Matters

This benchmark reveals the gap between fluent-sounding AI and actual understanding. It’s a stress test for intelligence, not just language fluency.

Most AI models excel at spotting patterns in massive datasets. But HLE demands true reasoning—multi-step logic, abstract thinking, and synthesis across domains. Can AI truly think like an expert human?

In short, Humanity’s Last Exam is less about showing off what AI knows—and more about revealing what it doesn’t. It’s a mirror held up to machine intelligence, and the reflection is humbling.

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