If the following scenario doesn’t already sound familiar, then it will soon. A kid is doing homework alone in another room. You hear a question, “What’s the capital of Delaware?” The parent starts thinking: Baltimore … too obvious … Wilmington … not a capital. But before the thought is complete, a machine called Alexa says the correct answer: “The capital of Delaware is Dover.” Alexa is Amazon’s artificial intelligence, or AI, that interprets natural language and provides answers to questions at lightning speed. Alexa has replaced the parent as the all-knowing source of information in the eyes of a child.
AI is everywhere. It’s in our phones, cars, shopping experiences, romantic matchmaking, hospitals, banks, and all over the media. No wonder corporate directors, CEOs, vice presidents, managers, team leaders, entrepreneurs, investors, coaches, and policymakers are anxiously racing to learn about AI: they all realize it is about to fundamentally change their businesses.
The three of us have observed the advances in AI from a distinctive vantage point. We are economists who built our careers studying the last great technology revolution: the internet. During years of research, we learned how to cut through the hype to focus on what technology means for decision-makers.
We also built the Creative Destruction Lab (CDL), a seed-stage program that increases the probability of success for science-based startups. Initially, the CDL was open to all kinds of startups, but by 2015, many of the most exciting ventures were AI-enabled companies. To our knowledge, as of September 2022, the CDL had, over eight years, the greatest concentration of AI startups of any program on earth.
As a result, many leaders in the field regularly traveled to Toronto to participate in the CDL. For example, one of the primary inventors of the AI engine that powers Amazon’s Alexa, William Tunstall-Pedoe, flew to Toronto every eight weeks from Cambridge, England, to join us throughout the duration of the program. So did San Francisco–based Barney Pell, who previously led an eighty-five-person team at NASA that flew the first AI in deep space.
The CDL’s dominance in this domain resulted partly from our location in Toronto, where many of the core inventions—in a field called “machine learning”—that drove the recent interest in AI were seeded and nurtured. Experts who were previously based in the computer science department at the University of Toronto today head several of the world’s leading industrial AI teams, including those at Meta (Facebook), Apple, and Open AI.
Being so close to so many applications of AI forced us to focus on how this technology affects business strategy. As we’ll explain, AI is a prediction technology, predictions are inputs to decision-making, and economics provides a perfect framework for understanding the trade-offs underlying any decision. So, by dint of luck and some design, we found ourselves at the right place at the right time to form a bridge between the technologist and the business practitioner. The result is this book.
Our first key insight is that the new wave of artificial intelligence does not actually bring us intelligence but instead a critical component of intelligence—prediction. What Alexa was doing when the child asked a question was taking the sounds it heard and predicting the words the child spoke and then predicting what information the words were looking for. Alexa doesn’t “know” the capital of Delaware. But Alexa is able to predict that, when people ask such a question, they are looking for a specific response: “Dover.”
Each startup in our lab is predicated on exploiting the benefits of better prediction. Deep Genomics improves the practice of medicine by predicting what will happen in a cell when DNA is altered. Ada improves customer service by predicting customer intent in online interactions. Validere improves the efficiency of oil custody transfer by predicting the water content of incoming crude. These applications are a microcosm of what most businesses will be doing in the near future.
If you’re lost in the fog trying to figure out what AI means for you, then we can help you understand the implications of AI and navigate through the advances in this technology, even if you’ve never programmed a convolutional neural network or studied Bayesian statistics.
If you are a business leader, we provide you with an understanding of AI’s impact on management and decisions. If you are a student or recent graduate, we give you a framework for thinking about the evolution of jobs and the careers of the future. If you are a financial analyst or venture capitalist, we offer a structure around which you can develop your investment theses. If you are a policymaker, we give you guidelines for understanding how AI is likely to change society and how policy might shape those changes for the better.
Economics provides a well-established foundation for understanding uncertainty and what it means for decision-making. As better prediction reduces uncertainty, we use economics to tell you what AI means for the decisions you make in the course of your business. This, in turn, provides insight into which AI tools are likely to deliver the highest return on investment for the workflows inside your business. This then leads to a framework for designing business strategies, such as how you might rethink the scale and scope of your business to exploit the new economic realities predicated on cheap prediction. Finally, we lay out the major trade-offs associated with AI on jobs, on the concentration of corporate power, on privacy, and on geopolitics.
What predictions are important for your business? How will further advances in AI change the predictions you rely on? How will your industry redesign jobs in response to advances in prediction technology just as industries reconfigured jobs with the rise of the personal computer and then of the internet? AI is new and still poorly understood, but the economics toolkit for evaluating the implications of a drop in the cost of prediction is rock solid; although the examples we use will surely become dated, the framework in this book will not. The insights will continue to apply as the technology improves and predictions become more accurate and complex.
Prediction Machines is not a recipe for success in the AI economy. Instead, we emphasize trade-offs. More data means less privacy. More speed means less accuracy. More autonomy means less control. We don’t prescribe the best strategy for your business. That’s your job. The best strategy for your company or career or country will depend on how you weigh each side of every trade-off. This book gives you a structure for identifying the key trade-offs and how to evaluate the pros and cons in order to reach the best decision for you. Of course, even with our framework in hand, you will find that things are changing rapidly. You will need to make decisions without full information, but doing so will often be better than inaction.
KEY POINTS
- The current wave of advances in artificial intelligence doesn’t actually bring us intelligence but instead a critical component of intelligence: prediction.
- Prediction is a central input into decision-making. Economics has a well-developed framework for understanding decision-making. The new and poorly understood implications of advances in prediction technology can be combined with the old and well-understood logic of decision theory from economics to deliver a series of insights to help navigate your organization’s approach to AI.
- There is often no single right answer to the question of which is the best AI strategy or the best set of AI tools, because AIs involve trade-offs: more speed, less accuracy; more autonomy, less control; more data, less privacy. We provide you with a method for identifying the trade-offs associated with each AI-related decision so that you can evaluate both sides of every trade in light of your organization’s mission and objectives and then make the decision that is best for you.