Power And Prediction Book Summary
The Disruptive Economics of Artificial Intelligence
Book by Ajay Agrawal, Joshua Gans, Avi Goldfarb
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Summary
"Power and Prediction" argues that the true potential of AI lies not in automating individual tasks, but in enabling the redesign of entire systems and decision-making processes, which will lead to significant shifts in economic and political power as AI evolves from a tool for prediction into a catalyst for transformation.
The Parable Of Three Entrepreneurs
The authors use the historical example of the slow adoption of electricity to illustrate the challenges of deploying a new general purpose technology like AI. They describe three types of entrepreneurs that tried to exploit electricity in different ways in the late 19th/early 20th century:
- Point solution entrepreneurs who simply replaced steam power with electric power with minimal factory redesign. This provided limited benefits.
- Application solution entrepreneurs who redesigned individual machines and tools around electric motors. This enabled some new capabilities but still limited benefits without factory redesign.
- System solution entrepreneurs who completely redesigned factories to fully exploit the unique advantages of electric power. This is what ultimately transformed manufacturing and the economy, but it took decades.
Section: 1, Chapter: 1
AI Adoption Faces The Same Challenges As Past General Purpose Technologies
The authors argue that AI is a general purpose technology (GPT) like electricity and the steam engine that has the potential to transform the economy over time. However, as with past GPTs, there is a significant delay between the initial invention and demonstration of the technology and its widespread adoption and impact on productivity. The authors refer to this delay as "The Between Times." During this period, point solutions and application solutions emerge, but the big productivity gains come only later with the development of system solutions that more fully exploit the technology's potential.
Section: 1, Chapter: 2
AI Is Fundamentally A Prediction Technology
The authors argue that the essence of recent advances in AI is that they represent a dramatic improvement in prediction - the ability to take information you have and generate information you don't have. Prediction is a key input into decision making. As prediction becomes cheaper, we will use more of it and the value of other inputs to decision making like human prediction will fall while the value of complements like data and judgment will rise. Judgment is determining the relative payoff or reward to different actions - it is a statement of what we want, while prediction tells us the likelihood of different outcomes.
Section: 1, Chapter: 3
"AIs Are Idiot Savants, Not General Intelligence"
"Predicting on the support of your data is not as simple as collecting data from a wider variety of settings to ensure you aren't extrapolating too much or avoiding predicting too far into the future. Sometimes the data you need doesn't exist. This underlies the refrain repeated in every statistics course worldwide: correlation is not necessarily causation."
Section: 1, Chapter: 3
Causal Inference Is Critical For Effective AI Deployment
When deploying AI systems, it's critical to ensure the predictions are valid for the decisions you want to make. Some key considerations:
- Distinguish between correlation and causation. AI predictions based on historical data may identify correlations that don't reflect causal relationships.
- Collect data that covers the full range of relevant situations. Predicting outside the "support of your data" is risky.
- Where possible, use randomized experiments to collect data that reliably measures causal impact. Leading tech companies now employ many economists and statisticians focused on causal inference.
Section: 1, Chapter: 3
2. Rules
Decisions Incur Cognitive Costs, While Rules Enable Reliability
The authors explain the difference between decisions and rules:
- Decisions allow you to take into account situational information but incur cognitive costs to gather information and deliberate
- Rules avoid cognitive costs but result in the same action regardless of the situation The key factors that determine if the cognitive cost of a decision is worthwhile are:
- The consequences of the decision - more consequential decisions are worth deciding
- The cost of information - if information is cheap, decisions become more attractive
Section: 2, Chapter: 4
AI Enables Turning Rules Into Decisions
As a leader, you should examine the existing rules and standard operating procedures in your organization and evaluate if they can be turned into decisions enhanced by AI predictions. Look for situations where:
- The rule leads to costly errors in some situations that could be mitigated by deciding differently
- AI can provide cheap, high-quality predictions to enable better situational decisions
- The cost savings or performance gains from better decisions justify the cognitive cost and reduced reliability of turning the rule into an AI-informed decision
Section: 2, Chapter: 4
Uncertainty Can Be Hidden By Rules And Expensive Accommodations
The authors use the example of modern airport design to illustrate the concept of "hidden uncertainty." Frequent air travelers arrive at the airport much earlier than their flights to accommodate the uncertainty around traffic, parking, security lines, flight delays, etc. Airports like Incheon Airport in South Korea now provide extensive amenities like spas, museums, gardens, and ice skating to make the inevitably long wait times more palatable.
However, this expensive infrastructure accommodates the hidden uncertainty rather than resolving it. The authors suggest that AI prediction could reduce the uncertainty and enable a new, more efficient equilibrium.
Section: 2, Chapter: 5
AI Navigation Apps Could Disrupt The Economics Of Airport Retail
Airport operators should be wary of the disruptive potential of AI-powered navigation apps like Waze and Google Maps. Key considerations:
- These apps can provide increasingly accurate, personalized predictions of travel time to the airport, reducing the need for passengers to budget large uncertainty buffers
- As passengers become more confident in "just in time" airport arrival, demand for in-terminal retail and dining may fall significantly
- Airport operators should explore ways to actively partner with navigation apps to shape behavior and preserve retail revenues, rather than being passive victims of disruption
Section: 2, Chapter: 5
Standard Operating Procedures Provide Reliability But Stifle AI-Enhanced Decisions
The authors present a framework for understanding the role of rules and standard operating procedures (SOPs) in organizations:
- SOPs reduce individual cognitive load by providing pre-specified actions
- SOPs enable reliability and predictability across an organization by ensuring different people/groups take consistent, coordinated actions without extensive communication
- SOPs "glue" different parts of an organization together in an interdependent system resistant to change
- Replacing SOPs with AI-enhanced decisions reduces reliability and predictability, unsticking the organizational glue
- Transforming rules into AI-enhanced decisions often requires systemic change to re-establish coordination in new ways
Section: 2, Chapter: 6
3. Systems
4. Power
5. How AI Disrupts
6. Envisaging New Systems
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