Title: The Case for Artificial Useful Intelligence (AUI) over AGI Resource URL: https://podcasts.apple.com/us/podcast/ep-481-the-case-for-artificial-useful-intelligence/id1683401861?i=1000699037844 Publication Date: 2025-03-13 Format Type: Podcast Reading Time: 35 minutes Contributors: Jordan Wilson;Ruchir Puri; Source: Everyday AI Podcast (YouTube) Keywords: [Artificial Intelligence, Business Strategy, Artificial Useful Intelligence, AI Agents, AI Workforce Reskilling] Job Profiles: Academic/Researcher;Machine Learning Engineer;Artificial Intelligence Engineer;Data Analyst;Chief Technology Officer (CTO); Synopsis: In this episode of the Everyday AI podcast, host Jordan Wilson speaks with computer scientist Ruchir Puri about why the pursuit of artificial useful intelligence (AUI) may be more important for businesses and individuals than chasing artificial general intelligence (AGI). Takeaways: [Intelligence should be understood as a blend of IQ, emotional intelligence (EQ), and relational intelligence (RQ), not just raw IQ., Businesses should focus on AI's utility in solving real-world problems rather than obsessing over achieving AGI., AI agents that self-correct and use external tools represent a profound next step in AI evolution., Business leaders must actively engage with AI technologies to form educated strategies for disruption and reconstruction.] Summary: Artificial Useful Intelligence (AUI) should be prioritized over Artificial General Intelligence (AGI) because usefulness directly impacts business and personal growth. Intelligence is multi-dimensional, comprising IQ, EQ, and RQ, and technologies that assist daily activities are more beneficial than those chasing abstract AGI ideals. Rusheer Piri recounts historical milestones such as IBM’s Deep Blue and Watson to illustrate the shifting nature of AI milestones, emphasizing that real impact lies in practical application. Modern AI advances, especially reasoning-enabled and agentic models, shift AI systems from simple input-output mechanisms to autonomous agents capable of iterative problem-solving and tool usage. This transition mirrors historical shifts like the Industrial Revolution’s automation of manual labor. In today's context, AI augments knowledge work rather than replacing it, creating new productivity channels. Businesses must have three critical approaches: leaders should gain hands-on familiarity with AI, develop strategic plans addressing both disruption and reconstruction, and prioritize reskilling teams to operate effectively alongside new AI tools. Future organizational structures will center around humans managing digital agents, reinforcing the enduring need for strong emotional and relational skills alongside technical know-how. Content: ## Introduction **Everyday AI** is a daily podcast and livestream dedicated to demystifying artificial intelligence (AI) and presenting practical guidance for enhancing careers, businesses, and personal productivity. Hosted by a seasoned AI commentator, the show offers an accessible lens on core concepts—ranging from generative AI to large language models (LLMs)—and explores their real-world applications. ## The AI Acronym Soup Listeners frequently encounter an alphabet of terms: AI, Gen AI, LLMs, AGI (artificial general intelligence), ASI (artificial superintelligence), and beyond. Although these labels generate excitement (and at times anxiety), the central question for decision makers and practitioners is this: Which AI capabilities deliver genuine day-to-day value? ## The Case for Artificial Useful Intelligence (AUI) Rather than chasing ill-defined milestones of “general” or “super” intelligence, a more pragmatic focus is on **artificial useful intelligence** (AUI). This concept emphasizes: 1. **Practical Utility**: Does the AI system solve concrete problems in business or daily life? 2. **Measurable Impact**: Are efficiencies, productivity, or risk mitigation demonstrably improved? 3. **Human Oversight**: Do humans retain clear accountability, governance, and control over outcomes? By orienting AI investments toward usefulness—rather than chasing hypothetical superintelligence—organizations can accelerate return on investment and minimize the cultural resistance that often accompanies radical technological disruption. ## Historical Milestones in AI ### 1. Deep Blue vs. Garry Kasparov (1997) - IBM’s Deep Blue was the first machine to defeat the reigning world chess champion, Garry Kasparov, in a regulated match. - Although this victory marked a watershed moment in specialized AI, it did not equate to human-like general intelligence. ### 2. Watson on Jeopardy (2011) - Fourteen years later, IBM Watson demonstrated its prowess on the quiz show *Jeopardy!*, outperforming human champions in real time. - Despite its impressive language parsing and retrieval algorithms, Watson remained an expert system, not a universal thinker. ### 3. The Ongoing Evolution Definitions of “general intelligence” have continually shifted. Some propose revenue thresholds (e.g., $100 billion), while others emphasize cognitive flexibility. In practice, no consensus has emerged—and every apparent milestone simply resets expectations. ## Deconstructing Intelligence: IQ, EQ, and RQ Human intelligence comprises multiple dimensions: - **IQ (Intelligence Quotient)**—analytical and logical reasoning - **EQ (Emotional Quotient)**—empathy, self-awareness, and interpersonal sensitivity - **RQ (Relationship Quotient)**—networking, collaboration, and influence AI efforts have historically emphasized IQ-style tasks: pattern recognition, complex problem solving, and logical deduction. Yet EQ and RQ remain squarely in the human domain, underpinning leadership, culture, and customer engagement. ## AI’s Impact on the Future of Work ### 1. From Factory Floors to Knowledge Work - The Industrial Revolution automated physical labor with machines (e.g., hammers to nail guns). - In recent decades, “knowledge work”—reading, writing, analysis—has been a prized human capability. ### 2. Language as a Frontier - Today’s LLMs can comprehend, generate, and translate language (including code) at scale. - This parallels past transitions: just as the hammer enabled more efficient construction, language-processing AI can accelerate research, customer service, and decision making. ## Reasoning Models and Agentic AI Traditional AI systems operate in a **feed-forward** manner: 1. You supply a prompt. 2. The model returns an answer. 3. If the response is unsatisfactory, you revise the prompt manually. By contrast, **agentic AI** leverages **feedback loops**: 1. **Self-assessment**: The agent compares its output against the original intent. 2. **Iterative refinement**: It adjusts its approach automatically until the goal is achieved. 3. **Tool orchestration**: It selectively invokes calculators, databases, or APIs for specialized subtasks. Analogy: Navigating a rocket to the moon requires continuous course corrections—one misalignment in launch will prevent arrival. Agentic AI applies similar feedback principles to maintain alignment with objectives. ## Practical Applications of AUI ### Software Engineering Agents - **Challenge**: Developers juggle extensive codebases, patch urgent bugs, and manage releases under tight deadlines. - **Solution**: An AI-driven engineering agent can: • Pinpoint the root cause of a reported issue in thousands of lines of code • Propose and implement a fix, complete with explanatory reasoning • Accelerate bug resolution by automating repetitive tasks, freeing developers for higher-level work ### AI for Security - **Challenge**: Cyber threats evolve rapidly, and human experts cannot monitor every vulnerability at scale. - **Solution**: Security-focused AI continuously scans software for weaknesses, triages risks, and recommends or applies patches—reducing exposure and strengthening defenses. ## Preparing Your Organization for AI 1. **Hands-On Familiarity**: Encourage leaders and teams to experiment with AI tools directly. Practical exposure fosters realistic expectations and creative use cases. 2. **Strategic Road-Mapping**: Develop a clear plan for how AI will disrupt and transform your business processes—beyond mere automation, consider entirely new service models. 3. **Skills Development**: Equip employees with the competencies to manage, govern, and collaborate with AI systems. Cultivate a culture of continuous learning to mitigate resistance. ## Conclusion and Resources Artificial useful intelligence (AUI) shifts the conversation from abstract milestones to tangible, measurable value. By focusing on practical outcomes, maintaining human accountability, and embracing agentic AI, organizations can unlock unprecedented productivity and innovation. For further insights, subscribe to the free daily newsletter at [yourEverydayAI.com](https://yourEverydayAI.com). You’ll receive concise AI news, expert interviews, and actionable recommendations straight to your inbox—ensuring you stay ahead in the fast-evolving AI landscape.