# Webinar: What AI Can and Cannot Do: Intelligence Augmentation in Practice with Michael Bernstein

## Executive summary

Professor Michael Bernstein argues that AI's value lies in 'Intelligence Augmentation' (IIA)—making humans smarter, not replacing them. He introduces a critical framework distinguishing between 'rough-edged' problems (those with many plausible solutions, like writing copy) and 'sharp-edged' problems (those with only one correct solution, like fixing a bug). The core finding is that AI struggles more with sharp-edged tasks, necessitating a focus on human-in-the-loop processes and designing for high error tolerance when building AI-powered systems.

## Key takeaways

- AI Success is Driven by Problem Framing: The set of solvable 'rough-edged' problems is always larger than the set of solvable 'sharp-edged' problems. When planning AI features, it is often more effective to convert a high-stakes, sharp-edged problem into a lower-stakes, rough-edged one that can provide useful, iterative drafts today. (Timestamp: 1628)
- Focus on Augmentation, Not Replacement: The most successful AI deployments are those that enhance human capabilities (complimentarity), where 'Human + AI' performs better than either human or AI alone. The goal should be to create a 'superpowered' human, not an autonomous replacement. (Timestamp: 2200)
- Understand Error Tolerance: For sharp-edged problems, the required accuracy threshold is extremely low; if the AI is even slightly error-prone, the system is unusable. For rough-edged problems, even a decent draft can be useful and iteratively improved by a human. (Timestamp: 1130)

## Technical details

- Sharp-Edged vs. Rough-Edged Problems: A **sharp-edged problem** has only one correct solution (e.g., fixing a bug, legal citation). A **rough-edged problem** has many different plausible solutions (e.g., writing marketing copy, designing a UI). AI struggles more with sharp-edged tasks due to high error sensitivity. (Timestamp: 820)
- Agentic AI and Tool Use: An agentic AI is a system that outputs tool commands, allowing it to use a library of tools (e.g., writing code, reading files, searching the web, sending emails) and decide the sequence of actions. Agents can operate in both sharp- and rough-edged capacities. (Timestamp: 1000)
- AI Reliability and Verification: Coding agents can tackle sharp-edged problems only if the outcome is automatically verifiable, allowing the agent to know when a link in the workflow has failed. Failure to verify leads to compounded errors. (Timestamp: 1200)
- Cognitive Biases in AI Use: Users often fall into 'overreliance' (trusting the AI too much) or 'algorithm aversion' (underrusting the AI after a mistake). Trust in algorithms is noted to be more brittle than trust in people. (Timestamp: 2000)

## Practical implications

- When designing AI features, prioritize converting high-stakes, sharp-edged requirements into iterative, rough-edged workflows that allow for human review and refinement.
- Design systems to explicitly manage the 'seam' or handoff between human and AI, as this boundary is a common point of failure (e.g., in self-driving cars).
- Measure success using metrics that reflect augmentation (e.g., product quality, market performance) rather than just replacement metrics (e.g., time saved, cost reduced).
- Build user interfaces that are integrated into existing workflows and are designed to be 'unremarkable' to minimize user resistance and maximize adoption.

## Topics

Artificial Intelligence (AI), Intelligence Augmentation (IIA), Product Strategy, Generative AI, System Reliability, Human-AI Interaction, AI-Powered Product Innovation Course

Source: https://www.youtube.com/watch?v=y4xvZnl102w