Artificial Intelligence is the new PC

Artificial Intelligence is making breakthrough advancements in image recognition, natural language processing, robotics, and machine learning in general. The company DeepMind, a leader in AI research, has created AlphaZero in 2018, an AI program that has reached superhuman performance in many games including the game of Go, and more recently in 2020, AlphaFold that solved a protein folding problem that has preoccupied researchers for 50 years. For Stanford Professor Andrew Ng, AI is the new electricity.

A more useful comparison would be the advent of the personal computer, in particular, the IBM PC in 1981, and its first killer application, the spreadsheet software Lotus 123. IBM didn’t introduce the first computer. Home computers were already available for hobbyists since 1977 from companies such as Commodore, Tandy, and Apple. The Apple II with Visicalc was especially already very popular but the IBM PC was the first affordable personal computer enthusiastically adopted by the business community.  

Figure 1. IBM PC

Figure 2. Lotus 123

The novel spreadsheet software allowed flexible free-form calculations, the automation of calculations, the use of custom functions, graphics, references, and data management. Excel, the dominant spreadsheet software is still in use more than thirty years after its first introduction (with more functionalities). Before the spreadsheets, people used calculators and reported results on paper. More intensive calculations were done with mainframe computers in a language such as FORTRAN and results were printed on paper.

Today, AI is the new PC. Not adopting AI is like forgoing the PC in 1981. The impact is already very profound among the native digital companies and should be as significant for the rest of the companies.

Today, business leaders need to think about an AI strategy as they have to think about their information technology strategy. Like the PC and the spreadsheet, they should expect all their employees to become at some point users of AI at work. As the home computer, AI is already present at home with personal assistants such as Amazon Alexa, on phones with Apple Siri, and the internet with Google. All these AI applications are now possible thanks to increasing computer power, the development of the cloud, the availability of big data, and the new machine deep learning paradigm.

The AI Strategy Handbook was written to help you adopt AI in your business strategy so that it creates a long-term sustainable competitive advantage for your customers, your company, your employees, and your investors.

The Impact of Machine Learning on Economics

Stanford Professor Susan Athey wrote a very detailed survey on the impact of machine learning on economics.


This paper provides an assessment of the early contributions of machine learning to economics, as well as predictions about its future contributions. It begins by briefly overviewing some themes from the literature on machine learning, and then draws some contrasts with traditional approaches to estimating the impact of counterfactual policies in economics. Next, we review some of the initial “off-the-shelf” applications of machine learning to economics, including applications in analyzing text and images. We then describe new types of questions that have been posed surrounding the application of machine learning to policy problems, including “prediction policy problems,” as well as considerations of fairness and manipulability. We present some highlights from the emerging econometric literature combining machine learning and causal inference. Finally, we overview a set of broader predictions about the future impact of machine learning on economics, including its impacts on the nature of collaboration, funding, research tools, and research questions.

She gave a talk at a 2017 NBER conference on the economics of AI:

Welcome to Rodeo AI

Rodeo AI is an economics blog on data science, machine learning and artificial intelligence (AI). We plan to explore the exciting field of AI, its recent developments, its applications and its consequences from an economist’s perspective. We want to explore how these new fields can help improve the economist’s work but also how economics can be useful to the data scientist, machine learning and AI specialist.