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The Growing Performance Gap in AI Usage
James Garner
February 24, 2025
Key Points
The performance gap between skilled and unskilled AI users is widening as AI models advance.
Expert prompting can yield 2-10x better results from the same AI compared to basic prompting.
Narrow AI is transforming specific fields like healthcare and finance while we wait for AGI.
Your ability to ask good questions will soon matter more than the AI model you use.
Ethan Mollick highlights narrow AI's impact and the importance of prompting skills.
As AI models become more sophisticated, the way users interact with them through prompting is crucial. Basic prompting, like simple questions, often leads to basic results, even with advanced models. However, expert prompting—using detailed instructions and techniques like chain-of-thought reasoning—can unlock significantly better outcomes, potentially 2 to 10 times more value from the same AI. This gap is growing because newer models have more potential, but only skilled users can fully utilise it.
Narrow AI's Revolution in Specific Fields
While everyone awaits artificial general intelligence (AGI), which would match human intelligence across all tasks, narrow AI is already making big changes in specific areas. Narrow AI is designed for single tasks, like diagnosing diseases in healthcare or detecting fraud in finance. It's transforming industries by improving efficiency and accuracy, and examples include AI in autonomous vehicles and personalized education.
Ethan Mollick's Insights
Ethan Mollick, a professor at the Wharton School, emphasizes that narrow AI is quietly revolutionizing fields while the focus remains on AGI. He also stresses that effective prompting is key to maximizing AI's benefits, especially as the performance gap widens. His work shows that businesses and individuals must adapt to use AI effectively.
Surprising Detail: Prompting Skills Outweigh Model Choice
It's surprising that soon, how well you ask questions might matter more than which AI model you use, highlighting the shift towards user skill over technology.
Comprehensive Analysis on AI Performance Gaps and Narrow AI Impacts
This detailed analysis explores the growing performance disparity between proficient and novice AI users, the critical role of expert prompting, and how narrow AI is transforming specific industries. It integrates insights from Ethan Mollick and other sources to provide a thorough understanding of these trends, ensuring a complete response to the topic at hand.
Introduction to AI and Prompting
In the rapidly evolving landscape of artificial intelligence, the interaction between users and AI models is becoming increasingly pivotal. The performance gap between those adept at prompting AI and those who are not is widening with each new model release. Prompting refers to the process of providing instructions or "prompts" to an AI model to guide its behavior and output. Basic prompting, characterized by simple, straightforward instructions, often yields basic results, even from advanced models. In contrast, expert prompting involves sophisticated techniques, such as providing detailed context, using specific formats, and employing advanced strategies like chain-of-thought reasoning, which can lead to significantly better outcomes.
Ethan Mollick, a professor at the Wharton School known for his work on AI's impact on business and society, argues that while the world awaits artificial general intelligence (AGI)—AI that matches human intelligence across all domains—narrow AI is quietly revolutionizing specific fields. Narrow AI, designed to perform specific tasks, is already making profound impacts in areas such as healthcare, finance, and transportation. This analysis will explore these aspects, highlighting the importance of prompting skills and the transformative effects of narrow AI.
Defining Narrow AI and AGI
To understand the current landscape, it's essential to distinguish between narrow AI and AGI:
Narrow AI (also known as weak AI): This type of AI is designed to perform specific tasks or solve particular problems within a limited context. It does not possess the ability to generalize its knowledge to other areas. Examples include voice assistants like Siri, recommendation algorithms on streaming services like Netflix, and AI-powered diagnostic tools in medicine. According to Narrow AI Definition | DeepAI, narrow AI is the most common form of AI encountered today, operating under pre-defined rules and lacking consciousness or genuine understanding.
Artificial General Intelligence (AGI): AGI refers to AI systems that can understand, learn, and apply intelligence across a wide range of tasks, similar to human intelligence. AGI would be capable of performing any intellectual task that a human can, from playing chess to composing music to solving complex problems in any field. Currently, all existing AI systems are narrow AI, and AGI remains a goal for future development, with ongoing research and debate about its feasibility and timeline, as noted in General AI vs Narrow AI.
Narrow AI's Revolution in Specific Fields
Despite the fascination with AGI, narrow AI is already making significant impacts across various sectors by providing specialized solutions to complex problems. The following table outlines key fields and examples of narrow AI applications:
Field | Examples of Narrow AI Applications |
|---|---|
Healthcare | Disease diagnosis via medical image analysis, personalized medicine based on genetic data |
Finance | Fraud detection in real-time, algorithmic trading using market data analysis |
Transportation | Autonomous vehicles for navigation, traffic management to optimize flow |
Manufacturing | Quality control with AI-powered robots, predictive maintenance to reduce downtime |
Education | Personalized learning adapting to student needs, automated grading and feedback |
Agriculture | Crop monitoring and disease detection, as per |
Entertainment | Recommendation systems for streaming services, AI opponents in strategy games |
Military | Surveillance and target recognition, enhancing operational efficiency |
These examples illustrate how narrow AI enhances efficiency, accuracy, and scalability, revolutionising industries by addressing specific challenges. For instance, in healthcare, AI algorithms can analyze X-rays and MRIs to detect diseases like cancer with high accuracy, as detailed in What Is Narrow AI and What Are Its Applications?. In finance, AI-powered systems detect suspicious transactions to prevent fraud in real-time, improving security and trust.
Ethan Mollick's perspective aligns with this, emphasizing that while the pursuit of AGI continues, narrow AI is already transforming these fields. His work, such as the study "Navigating the Jagged Technological Frontier" (Ethan Mollick – Management Department), examines AI's performance implications on complex tasks, suggesting that narrow AI's impact is significant and underrecognized compared to the AGI focus.
The Role of Prompting in AI Performance
Prompting is the process of providing instructions to an AI model, and its quality significantly affects the results obtained. The web search on "importance of prompting in AI" revealed that well-crafted prompts lead to accurate and relevant outputs, while poor prompts can result in vague or incorrect information. Key insights include:
Basic Prompting: Simple instructions, such as "summarize this article," may not fully utilize the AI's capabilities, leading to generic outputs. According to Effective Prompts for AI: The Essentials - MIT Sloan, basic prompting is akin to having a conversation without context, limiting the AI's effectiveness.
Expert Prompting: Techniques like chain-of-thought reasoning, where the AI is encouraged to think step-by-step, and providing examples can enhance results. For instance, asking, "Perform a regression analysis on this dataset to predict sales based on advertising spend, and provide a summary including correlations and recommendations," yields detailed, actionable insights. AI Prompting Best Practices | Codecademy highlights that providing context and being concise are crucial for effective prompts.
The article from [The Algorithmic Bridge](https://www.thealgorithmicbridge.com/p/agi-is-already-hereits-just-not-evenly?_bhlid=dee427a3ead a5b4e4147f8145e0fe3c71c1db317) further supports this, noting that expert prompting can unlock 2x to 10x performance gains, with tools like Deep Research ($200/month) showing "outsized gains" with proper prompts. It mentions techniques like "let’s think step-by-step" and "wait" prompting, as seen in X posts and academic papers like Chain-of-Thought Prompting and Wait Prompting.
Growing Performance Gap and Why It Matters
As AI models become more powerful and complex, the performance gap between skilled and unskilled users is widening. Several factors contribute to this:
Increased Model Capabilities: Newer models, such as GPT-4o and o3, have greater potential, but unlocking it requires nuanced prompts. The article from [The Algorithmic Bridge](https://www.thealgorithmicbridge.com/p/agi-is-already-hereits-just-not-evenly?_bhlid=dee427a3ead a5b4e4147f8145e0fe3c71c1db317) shows performance following S-curves, with models having different y-intercepts, asymptotes, and slopes, indicating varying limits that expert prompting can approach.
Complexity of Tasks: As AI is used for more complex tasks, the need for precise instructions increases, making basic prompting less effective. For example, in healthcare, a doctor needs to provide detailed patient data for accurate diagnosis, as noted in What is an AI Prompt? | TechTarget.
Learning Curve: The techniques for effective prompting are evolving, and those who invest time in learning gain a competitive advantage. A guide to prompting AI suggests that prompting skills may be temporary, but currently, they are crucial for leveraging AI.
Feedback Loop: Users who get better results are more likely to refine their skills, further widening the gap. This is evident in testimonies from X posts linked in [The Algorithmic Bridge](https://www.thealgorithmicbridge.com/p/agi-is-already-hereits-just-not-evenly?_bhlid=dee427a3ead a5b4e4147f8145e0fe3c71c1db317), such as Deep Research feedback.
This gap suggests that in the future, the ability to craft effective prompts will be more important than the specific AI model used, a point Mollick emphasizes in his blog One Useful Thing, noting that user skill will determine access to AI's potential.
Ethan Mollick's Contributions and Broader Implications
Ethan Mollick's work provides valuable insights into these trends. His book "Co-Intelligence: Living and Working with AI" (Co-Intelligence: An AI Masterclass with Ethan Mollick | Stanford GSB) and blog posts, such as Acceleration, highlight the rapid pace of AI development and its practical applications. His research, including "Navigating the Jagged Technological Frontier" (Ethan Mollick – Management Department), shows AI's impact on productivity, underscoring that benefits require organizational changes and skilled prompting.
Mollick's argument, as interpreted from the user's query, aligns with the idea that while AGI is a future goal, narrow AI is already transforming fields like healthcare and finance, as seen in the examples provided. His emphasis on prompting skills resonates with the performance gap discussion, suggesting that businesses and individuals must adapt to leverage AI effectively, avoiding being part of the "AI-poor crowd," as mentioned in The Algorithmic Bridge.
Conclusion
In conclusion, the performance gap between good and bad AI users is growing, driven by the increasing importance of expert prompting. As AI models advance, the ability to formulate precise and effective prompts will become a key differentiator, potentially outweighing the choice of model. Simultaneously, narrow AI is revolutionizing specific fields, providing unprecedented capabilities in healthcare, finance, transportation, manufacturing, education, agriculture, entertainment, and military, as detailed in various sources like AI revolutionizing industries worldwide. While the allure of AGI persists, it's crucial to recognize and harness the power of narrow AI in its current form. For individuals and organizations, investing in developing prompting skills and understanding how to best utilize narrow AI will be essential for success in the AI-driven future.
Key Citations
[AGI Is Already Here—It’s Just Not Evenly Distributed - The Algorithmic Bridge](https://www.thealgorithmicbridge.com/p/agi-is-already-hereits-just-not-evenly?_bhlid=dee427a3ead a5b4e4147f8145e0fe3c71c1db317)
Co-Intelligence: AI Masterclass with Ethan Mollick - Stanford GSB