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Federal Reserve's Cook Acknowledges AI's Transformative Impact and Potential for Short-Term Unemployment

Federal Reserve Governor Lisa Cook has commented on the significant changes being triggered by artificial intelligence (AI). Cook's remarks highlight the broad impact of AI across various sectors and suggest a potential rise in unemployment in the short term as industries adapt to these technological advancements. These observations underscore the ongoing discussion among policymakers and economists regarding the economic implications of rapid AI integration.

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Federal Reserve Governor Lisa Cook has made comments regarding the profound changes being brought about by artificial intelligence (AI). Cook indicated that AI is triggering substantial transformations across the economy. Furthermore, she noted the possibility of a rise in unemployment in the short term as a consequence of these shifts. These statements reflect a growing recognition within economic policy circles of AI's dual impact: its potential to drive innovation and productivity, alongside the challenges it poses to labor markets and employment stability during periods of transition.

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Meituan Showcases AI Innovations at ACL 2026: Advancing LLM Evaluation, Reasoning, and Generative Recommendations
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Meituan Showcases AI Innovations at ACL 2026: Advancing LLM Evaluation, Reasoning, and Generative Recommendations

The Meituan technical team has achieved significant recognition at the ACL 2026 conference, with six papers accepted into this premier international forum for computational linguistics and natural language processing. These research contributions span critical frontiers in the AI landscape, including large language model (LLM) capability evaluation, complex process reasoning, and the optimization of competition-level mathematical thinking. Additionally, the papers explore advancements in reinforcement learning and the evolution of generative recommendation systems. By addressing these diverse technical directions, Meituan is actively shaping a new paradigm for generative AI, focusing on bridging the gap between theoretical research and practical industrial applications. This selection of papers highlights Meituan's commitment to enhancing model intelligence and reasoning capabilities to solve sophisticated real-world problems.

Meituan LongCat Releases General 365: A New Benchmark for AI Reasoning Evaluation
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Meituan LongCat Releases General 365: A New Benchmark for AI Reasoning Evaluation

Meituan's LongCat team has officially launched General 365, a rigorous new benchmark designed to evaluate the reasoning capabilities of large language models. In a comprehensive test of 26 mainstream models, the results revealed a significant performance gap in the industry. Even the top-performing model, Gemini 3 Pro, achieved an accuracy rate of only 62.8%. Furthermore, the vast majority of the models tested failed to reach the 60% threshold, which is considered the passing mark for this evaluation. This release sets a challenging new standard for AI development, highlighting that complex reasoning remains a major hurdle for even the most advanced artificial intelligence systems currently available.

Managing AI-Driven Development: Meituan’s Strategy for Refactoring 310,000 Lines of Code Using Agent Evaluation Logic
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Managing AI-Driven Development: Meituan’s Strategy for Refactoring 310,000 Lines of Code Using Agent Evaluation Logic

Meituan's technical team has shared a comprehensive analysis of their experience refactoring 310,000 lines of code in an environment where over 90% of code is AI-generated. The core insight is that while AI significantly accelerates code production, it can also amplify technical debt and systemic chaos without proper constraints. To mitigate this, the team adopted an 'Agent evaluation' mindset to manage AI coding. By implementing a framework consisting of technical debt sorting, rule construction, standardized operating procedures (SOPs), and a Pre-PR (Pull Request) mechanism, they successfully transformed large-scale refactoring from a high-cost, specialized effort into a continuous, daily iterative process. This approach ensures that AI remains a productive tool rather than a source of unmanaged complexity.