Irving Wladawsky-Berger

Culture: The Software of the Mind

Several weeks ago I listened to a very interesting Freakonomics podcast hosted by Stephen Dubner. “According to a decades-long research project, the U.S. is not only the most individualistic country on earth; we’re also high on indulgence, short-term thinking, and masculinity,” said Dubner. The podcast included comments from a number of social scientists but was particularly focused on the work of Gert Jan Hofstede, social sciences professor  at Wageningen University in the Netherlands, whose research is focused on artificial sociality, - the study of human social behavior based on computational models. He's continued to lead a major research project on national cultures started more than 50 years ago by his father Geert Hofstede who passed away in 2020.

“Every person carries within him- or herself patterns of thinking, feeling, and potential acting that were learned throughout the person’s lifetime,” wrote the Hofstedes in their 2011 book Cultures and Organizations: Software of the Mind, coauthored with Michael Minkov. “Much of it was acquired in early childhood, because at that time a person is most susceptible to learning and assimilating. As soon as certain patterns of thinking, feeling, and acting have established themselves within a person’s mind, he or she must unlearn these patterns before being able to learn something different, and unlearning is more difficult than learning for the first time.”

The thinking patterns that guide our behaviors can be thought of as mental programs, a kind of software of the mind. “This does not mean, of course, that people are programmed the way computers are,” said the authors. “A person’s behavior is only partially predetermined by his or her mental programs: he or she has a basic ability to deviate from them and to react in ways that are new, creative, destructive, or unexpected.” Culture, the software of the mind, “only indicates what reactions are likely and understandable, given one’s past.”

In the podcast, Gert Jan Hofstede discussed the power of culture: “It is what we got fed with our mother’s milk and the porridge that our dad gave us. … If you’re part of a society, you’re like one drop in the Mississippi River. You may decide to go another way, but that doesn’t make the river change. So we’re all constraining one another through our collective culture.”

The Disruptive Economics of AI

In 2017, I attended a seminar by University of Toronto professor Avi Goldfarb on the economic value of AI. Goldfarb explained that the best way to assess the impact of a potentially transformative technology is to look at how the technology reduces the cost of a widely used function. Computers, for example, have dramatically reduced the cost of digital operations like arithmetic by several orders of magnitude. As a result, we’ve learned to define all kinds of tasks in terms of digital operations, e.g., financial transactions, inventory management, word processing, photography. Similarly, the internet has reduced the cost of communications and the Web has reduced the cost of access to information, which has led to a huge increase in applications based on communications and information, like music and video streaming, and digital media.

Viewed through this lens, AI is essentially a prediction technology, and its economic impact is to reduce the cost and expand the number and variety of applications that rely on predictions. A key finding of Stanford’s 2022 AI Index report was that AI is becoming much more affordable and higher performing, leading to the widespread commercial adoption of AI-based applications. “Since 2018, the cost to train an image classification system has decreased by 63.6%, while training times have improved by 94.4%.,” said the report.

Goldfarb and his U of T colleagues Ajay Agrawal, and Joshua Gans published their research in the 2018 book Prediction Machines: The Simple Economics of Artificial Intelligence. And this week, they just published Power and Prediction: The Disruptive Economics of Artificial Intelligence.

“When we published Prediction Machines in 2018, we thought we had said all we needed to on the economics of AI,” wrote the authors in the Preface of their new book. “We were wrong. We laid out a framework for the economics of AI in that book, which remains useful today. However, the Prediction Machines framework only told part of the story, the point solutions part. In the years since, we discovered that another key part of the AI story had yet to be told, the systems part. We tell that story here.”

The Changing Structure of Global Supply Chains

“The golden age of globalisation, in 1990-2010, was something to behold,” wrote The Economist in a January, 2019 article.  “Commerce soared as the cost of shifting goods in ships and planes fell, phone calls got cheaper, tariffs were cut and the financial system liberalised.” The very nature of the global firm was transformed during these two decades, with The Globally Integrated Enterprise destined to become the corporate model of the future, said IBM CEO Sam Palmisano in a 2006 Foreign Affairs article.

But, then global trade started slow down. “After the go-go 1990s and 2000s the pace of economic integration stalled in the 2010s, as firms grappled with the aftershocks of a financial crisis, a populist revolt against open borders and President Donald Trump’s trade war,” wrote The Economist in its June 18, 2022 lead article. “The flow of goods and capital stagnated. Many bosses postponed big decisions on investing abroad: just-in-time gave way to wait-and-see. No one knew if globalisation faced a blip or extinction.”

“Now the waiting is over, as the pandemic and war in Ukraine have triggered a once-in-a-generation reimagining of global capitalism in boardrooms and governments,” added the article. “Everywhere you look, supply chains are being transformed, from the $9trn in inventories, stockpiled as insurance against shortages and inflation, to the fight for workers as global firms shift from China into Vietnam. This new kind of globalisation is about security, not efficiency: it prioritises doing business with people you can rely on, in countries your government is friendly with.”

Most everyone agrees that global supply chains will be restructured over the next decade, accelerating the changes that were already in motion. “Decision-makers are increasingly concerned that supply chains should be robust, not just efficient,” said the briefing article in the same Economist issue. “As a result they are choosing to depend less on jurisdictions where they are exposed to risk. And countries are experimenting with industrial policies aimed at self-reliance or international pre-eminence in at least some ‘strategic’ technologies and businesses.” Let me discuss each of these potential changes.

The AI Maturity Framework

I recently attended a seminar, The Art of AI Maturity, by Accenture executives Philippe Roussiere and Praveen Tanguturi as part of MIT’s Initiative on the Digital Economy (IDE) lunch seminar series. The seminar was based on their recently published article The Art of AI Maturity: Advancing from Practice to Performance.

“Today, so much of what we take for granted in our daily lives stems from machine learning,” wrote the authors in the article’s executive summary. “Every time you use a wayfinding app to get from point A to point B, use dictation to convert speech to text, or unlock your phone using face ID ... you're relying on AI. And companies across industries are also relying on - and investing in - AI to drive logistics, improve customer service, increase efficiency, empower employees and so much more.”

To determine the true state of AI maturity in the marketplace, Accenture conducted a survey in August and September of 2021 of over 1,600 C-suite executives at nearly 2,000 of the world’s largest companies across 16 industries with headquarters in 15 countries. In addition, they interviewed 25 CEOs, Chief Data Officers and Chief Analytics Officers, as well as a number of Accenture AI experts, and developed over 40 case studies on AI transformation.