As 2023 comes to a close, it has become evident that the once-unstoppable global AI race is quickly transforming into a mad dash.
Just this month, The Arena Group, which operates Sports Illustrated, unceremoniously fired its CEO Ross Levinsohn weeks after the publication was found to have published articles written by fake authors with AI-created biographies and headshots. (The Arena Group has insisted that Levinsohn’s ouster “had absolutely nothing to do with the AI issue at all.”)
Google, too, is facing a storm of controversy for releasing a heavily edited first-look video of Gemini, the company’s most advanced AI yet. The prerecorded demo showed a bombastic, real-time, in-voice interaction between a person and Gemini. That clip later turned out to be an exaggeration. On the public policy front, the European Union recently put forward a set of far-reaching regulations that rank and limit the use of various AI systems, with large penalties in case of noncompliance. And of course, there was OpenAI’s very public dismissal, and subsequent reinstatement, of CEO Sam Altman—a saga that only highlighted the growing rift in Silicon Valley around AI safety. All is clearly not well in paradise.
Nevertheless, history shows that most disruptive innovations follow a similar pattern of turmoil before they are accepted as mainstream solutions to humanity’s problems. For example, at its dawn, the internet was riddled with malicious actors, policy challenges, and controversies. Copyright issues were rampant, with the heavy metal band Metallica famously suing the file-sharing network Napster for infringement, something that is now rearing its head with generative AI. Similarities between the early critique of search engines and modern AI are also remarkably close. These include bias and opacity in search results, breaches in privacy, as well as concerns around democracy and free speech. On the public policy front, governments of all shapes and sizes have long favored overreach when it comes to telecom networks and smartphones, despite glaring privacy issues. Yet, despite all this, these technologies have become a centerpiece of the human experience and, in most cases, unrecognizable from their initial state at launch.
So, what can we learn from these lessons of the past? As with all disruptive innovations, first and foremost, trust needs to be earned. This means that transparency and authenticity are critical for organizations that intend to use AI in any shape and form. Levinsohn’s firing from Sports Illustrated parent The Arena Group might not have happened if the AI-produced articles had not been brushed under the carpet. Gemini’s inflated demo, potentially created for a boost in stock price and public perception, did not yield noticeable results for Google’s market capitalization. OpenAI’s firing of Sam Altman might have been better handled and potentially adhered to if the board was clearer about why it chose to do so. A lack of transparency is simply not worth it.
Secondly, companies should realize that despite the hyperbole around the capabilities AI brings to the table, most of them will struggle to become an “AI-first” organization on their first try. Look, for example, at the wave of digital transformations that swept through the business world in the early 2010s. Digital transformation is a deliberate process that changes an organization’s offerings and operations, and eventually its business model, to a “digital-first” one. Such a shift creates lots of value—but it takes time, and companies might not see much success on day one.
Becoming an “AI-first” organization is likely to be a much bigger undertaking than a vanilla digital transformation (the former’s ethical issues are more pronounced and its future state is even hazier). Depending on the industry, a first-mover advantage in AI transformation might not be a great idea. Companies are better off running their AI transformations in bite-sized pieces, instead of going for a wholesale change. And always, authenticity and transparency are the only true currencies of success.
Hamza Mudassir is a fellow and lecturer in strategy at the University of Cambridge‘s Judge Business School.
As 2023 comes to a close, it has become evident that the once-unstoppable global AI race is quickly transforming into a mad dash.
Just this month, The Arena Group, which operates Sports Illustrated, unceremoniously fired its CEO Ross Levinsohn weeks after the publication was found to have published articles written by fake authors with AI-created biographies and headshots. (The Arena Group has insisted that Levinsohn’s ouster “had absolutely nothing to do with the AI issue at all.”)
Google, too, is facing a storm of controversy for releasing a heavily edited first-look video of Gemini, the company’s most advanced AI yet. The prerecorded demo showed a bombastic, real-time, in-voice interaction between a person and Gemini. That clip later turned out to be an exaggeration. On the public policy front, the European Union recently put forward a set of far-reaching regulations that rank and limit the use of various AI systems, with large penalties in case of noncompliance. And of course, there was OpenAI’s very public dismissal, and subsequent reinstatement, of CEO Sam Altman—a saga that only highlighted the growing rift in Silicon Valley around AI safety. All is clearly not well in paradise.
Nevertheless, history shows that most disruptive innovations follow a similar pattern of turmoil before they are accepted as mainstream solutions to humanity’s problems. For example, at its dawn, the internet was riddled with malicious actors, policy challenges, and controversies. Copyright issues were rampant, with the heavy metal band Metallica famously suing the file-sharing network Napster for infringement, something that is now rearing its head with generative AI. Similarities between the early critique of search engines and modern AI are also remarkably close. These include bias and opacity in search results, breaches in privacy, as well as concerns around democracy and free speech. On the public policy front, governments of all shapes and sizes have long favored overreach when it comes to telecom networks and smartphones, despite glaring privacy issues. Yet, despite all this, these technologies have become a centerpiece of the human experience and, in most cases, unrecognizable from their initial state at launch.
So, what can we learn from these lessons of the past? As with all disruptive innovations, first and foremost, trust needs to be earned. This means that transparency and authenticity are critical for organizations that intend to use AI in any shape and form. Levinsohn’s firing from Sports Illustrated parent The Arena Group might not have happened if the AI-produced articles had not been brushed under the carpet. Gemini’s inflated demo, potentially created for a boost in stock price and public perception, did not yield noticeable results for Google’s market capitalization. OpenAI’s firing of Sam Altman might have been better handled and potentially adhered to if the board was clearer about why it chose to do so. A lack of transparency is simply not worth it.
Secondly, companies should realize that despite the hyperbole around the capabilities AI brings to the table, most of them will struggle to become an “AI-first” organization on their first try. Look, for example, at the wave of digital transformations that swept through the business world in the early 2010s. Digital transformation is a deliberate process that changes an organization’s offerings and operations, and eventually its business model, to a “digital-first” one. Such a shift creates lots of value—but it takes time, and companies might not see much success on day one.
Becoming an “AI-first” organization is likely to be a much bigger undertaking than a vanilla digital transformation (the former’s ethical issues are more pronounced and its future state is even hazier). Depending on the industry, a first-mover advantage in AI transformation might not be a great idea. Companies are better off running their AI transformations in bite-sized pieces, instead of going for a wholesale change. And always, authenticity and transparency are the only true currencies of success.
Hamza Mudassir is a fellow and lecturer in strategy at the University of Cambridge‘s Judge Business School.