Summary of https://www.nature.com/articles/s44271-025-00258-x

Explores the emotional intelligence capabilities of Large Language Models (LLMs), specifically their ability to solve and create emotional intelligence tests. It highlights that several LLMs, including ChatGPT-4, consistently outperformed human averages on various established emotional intelligence assessments.

The research also investigated LLMs' capacity to generate new, psychometrically sound test items, finding that these AI-created questions demonstrated comparable difficulty and a strong correlation with original human-designed tests. While some minor differences were observed in clarity, realism, and content diversity, the study ultimately suggests that LLMs can reason accurately about human emotions and their regulation, indicating their potential for use in socio-emotional applications and psychometric development.

• LLMs demonstrate superior performance in solving emotional intelligence tests compared to humans. Six widely used Large Language Models (LLMs), including ChatGPT-4, ChatGPT-o1, Gemini 1.5 flash, Copilot 365, Claude 3.5 Haiku, and DeepSeek V3, collectively achieved an average accuracy of 81% on five standard emotional intelligence (EI) tests, significantly outperforming the human average of 56% reported in original validation studies. All tested LLMs scored more than one standard deviation above the human mean, with ChatGPT-o1 and DeepSeek V3 exceeding two standard deviations above it.
• LLMs are proficient at generating new, high-quality emotional intelligence test items. ChatGPT-4 successfully generated new test items (scenarios and response options) for five different ability EI tests, and these new versions demonstrated statistically equivalent test difficulty compared to the original tests when administered to human participants. Importantly, ChatGPT-4 did not simply paraphrase existing items; participants perceived a low level of similarity to any original test scenario in 88% of the newly created scenarios.
• LLM-generated tests exhibit psychometric properties largely comparable to original human-designed tests, though with some minor differences. While not all psychometric properties (such as perceived item clarity, realism, item content diversity, internal consistency, and correlations with vocabulary or other EI tests) were statistically equivalent between original and ChatGPT-generated versions, any differences observed were small (Cohen’s d less than ±0.25) and none of the 95% confidence interval boundaries exceeded a medium effect size (d ± 0.50). Furthermore, original and ChatGPT-generated tests were strongly correlated (r=0.46), suggesting they measure similar constructs.
• LLMs show potential for "cognitive empathy" and consistent application of emotional knowledge. The findings support the idea that LLMs can generate responses consistent with accurate knowledge of emotional concepts, emotional situations, and their implications, indicating they fulfill the aspect of cognitive empathy. LLMs offer advantages such as processing emotional scenarios based on extensive datasets, which may lead to fewer errors, and providing consistent emotional knowledge unaffected by human variability like mood, fatigue, or personal preferences.
• LLMs can significantly aid psychometric test development but cannot fully replace human validation processes. The research highlights that LLMs like ChatGPT can be powerful tools for assisting in the psychometric development of standardized assessments, particularly in the domain of emotion, by generating complete tests with generally acceptable psychometric properties using few prompts. However, the study also notes that while valuable for creating an initial item pool, LLMs cannot replace the necessary pilot and validation studies to refine or eliminate poorly performing items.

Summary of https://cookbook.openai.com/examples/agents_sdk/multi-agent-portfolio-collaboration/multi_agent_portfolio_collaboration

This guide from OpenAI introduces a multi-agent collaboration system built using the OpenAI Agents SDK, specifically designed for complex tasks like investment research. It demonstrates a "hub-and-spoke" architecture where a central Portfolio Manager agent orchestrates specialized agents (Macro, Fundamental, Quantitative) as callable tools.

The system leverages various tool types, including custom Python functions, managed OpenAI tools like Code Interpreter and WebSearch, and external MCP servers, to provide deep, high-quality analysis and scalable workflows. The document emphasizes modularity, parallelism, and auditability through structured prompts and tracing, offering a blueprint for building robust, expert-collaborative AI systems.

• Multi-Agent Collaboration is Essential for Complex Tasks The core concept is that multiple autonomous LLM agents can coordinate to achieve overarching goals that would be difficult for a single agent to handle. This approach is particularly useful for complex systems, such as financial analysis, where different specialist agents (e.g., Macro, Fundamental, Quantitative) can each handle a specific subtask or expertise area.
• The "Agent as a Tool" Pattern is Highly Effective This guide specifically highlights and uses the "agent as a tool" collaboration model. In this pattern, a central agent (the Portfolio Manager) orchestrates the workflow by calling other specialist agents as if they were tools for specific subtasks. This design maintains a single thread of control, simplifies coordination, ensures transparency, and allows for parallel execution of sub-tasks, which is ideal for complex analyses.
• Modular Design Fosters Specialization, Parallelism, and Maintainability Breaking down a complex problem into specialized agents, each with a clear role, leads to deeper, higher-quality research because each agent can focus on its domain with the right tools and prompts. This modularity also makes the system easier to update, test, or improve without affecting other components, and allows independent agents to work concurrently, dramatically reducing task completion time.
• Flexible Integration of Diverse Tool Types Enhances Agent Capabilities The OpenAI Agents SDK provides significant flexibility in defining and using various tool types. Agents can leverage custom Python functions for domain-specific logic, managed tools like Code Interpreter (for quantitative analysis) and WebSearch (for real-time information), and external MCP (Model Context Protocol) servers for standardized access to external data sources like Yahoo Finance.
• Structured Orchestration and Observability are Crucial for Robust Systems The Head Portfolio Manager agent's system prompt is central to the workflow, encoding the firm's philosophy, clear tool usage rules, and a multi-step process. This ensures consistent, auditable, and high-quality outputs. Furthermore, OpenAI Traces provide detailed visibility into every agent and tool call, allowing for real-time monitoring, debugging, and full transparency of the workflow.

Summary of https://media-publications.bcg.com/BCG-Executive-Perspectives-AI-First-Companies-Win-the-Future-Issue1-10June2025.pdf

This Boston Consulting Group (BCG) Executive Perspectives document, from June 2025, addresses how companies can become "AI-first" to achieve future success. It explains that the democratization of AI, shifting business economics, and the ability of AI-native firms to scale rapidly with lean teams necessitate this transformation.

The report details five key characteristics of an AI-first organization: a wider competitive moat, a reshaped profit and loss (P&L) model, a decentralized tech foundation, an AI-first operating model, and specialized, scalable talent.

It also provides five actionable steps for executives to begin their AI transformation journey, emphasizing a business-led AI agenda and the importance of demonstrating measurable impact.

• Wider Competitive Moat Companies will increase their ability to capitalize on key assets such as brand, intellectual property (IP), and talent. Brand trust, direct relationships with customers, ownership of innovations (including patents, trademarks, and copyrights), and exclusive, high-quality data sets become crucial as AI democratizes access and commoditizes content and advice.
• Reshaped P&L Model There will be high technology spending to support AI, with the value unlocked from efficiencies being reinvested. This involves a significant increase in tech spending (estimated 25-45%) and a decline in labor spending as AI reduces reliance on human-driven processes, ultimately boosting operating margins by redeploying value into growth priorities.
• Decentralized Tech Foundation Business units will be empowered to lead AI adoption and deploy AI solutions with increased speed and independence, while IT provides and maintains enterprise-wide AI platforms, agent ecosystems, and the overall tech, data, and cyber foundation.
• AI-First Operating Model Organizations will streamline their operations through reusable AI workflows and reduced duplication. This model shifts from traditional, people-centric processes supplemented by digital tools to processes built around AI agents, with human oversight for gap closure. This leads to flattened hierarchies, real-time governance, and an AI-embracing culture.
• Specialized, Scalable Talent Companies will develop lean, high-performing teams with specialized skills, focusing roles on judgment, strategy, and human-AI collaboration. AI will automate routine tasks, reshaping roles and potentially reducing headcount, while increasing productivity for top performers and intensifying the competition for skilled AI-fluent talent who will command a premium.