AI, in addition to its countless advantages, has an unexpected negative trait: it exacerbates systemic inequality. We fear a future where robots steal our jobs, but what will happen when a significant portion of the workforce ends up in algorithmically managed jobs?The classic success story is a leader who started modestly, in a low-level position at a post office, cash register, or factory. Although it was not easy, upward mobility was quite possible in traditional organizations. Mary Barra, Chair and CEO of General Motors, herself started on the
assembly line. There are also domestic examples: Aidyn Rakhimbaev began his journey as a millionaire while working as a general laborer.By comparison, how many Bolt drivers, in your opinion, will ever get the chance to take a managerial position in the company, let alone become CEO? How many future top managers at Wildberries will start their careers with delivering packages or assembling orders? Who will be able to repeat the path of Instacart founder Apurva Mehta, who personally fulfilled the company's first order?There is a "code ceiling" hindering career advancement — regardless of gender or race — in an organization based on artificial intelligence, junior employees and freelancers rarely interact with other human colleagues. Instead, they are managed by algorithms.Now, as people work under the supervision of artificial intelligence, information is usually distributed hierarchically: the company itself chooses what data to share with employees. In classic taxi services, drivers openly talked with each other and with the dispatcher — but when you work for Uber or Yandex.Taxi, the interactions are determined by a program designed to maximize efficiency and profit.Algorithmic management means constant monitoring and surveillance. Chinese Meituan or Ele.me
use algorithms to understand how much time it takes for couriers to deliver orders. The algorithm can reduce salary by penalizing an employee if they fail to meet the deadline. Amazon distribution center workers are also closely
watched by AI: they must walk at an "Amazon pace," described as "somewhere between walking and running."In the gig economy, you have to think not only about AI bosses but also about colleague-competitors. For example, Chicago residents discovered smartphones hanging from trees near Amazon sorting centers and Whole Foods stores. It turned out that contract couriers, trying to be the first to receive an order,
came up with this: if you hang a phone on a nearby tree, you can supposedly outsmart the algorithm.The labor market is becoming increasingly polarized: middle-skill jobs are shrinking compared to entry-level work (low-skilled work) and work requiring a higher level of qualification. The Covid-19 crisis likely accelerated this process. A global low-paid algorithmic workforce is already emerging. Whether in retail or financial services, logistics or manufacturing, AI-driven organizations are managed by a small group of highly paid employees, supported by complex automation and potentially millions of algorithmically managed low-paid freelancers on the periphery.The long-term solution to algorithmic inequality will lie not only in taxation and regulation but, rather, in our ability to ensure an adequate education system for the 21st century. Overhauling education will not be easy. Instead of looking for ways to use AI in learning, the question arises: how to teach people to use machine intelligence in
their careers? And how to teach people to be ready for continuous learning and retraining throughout their lives?Corporate leaders are called upon to play a decisive role. They should not only find channels for communication, feedback, and advancement for peripheral workers but also take retraining and team engagement seriously. For example, AT&T
is retraining half of its employees, while Cisco, IBM, Caterpillar, McKinsey, and JPMorgan offer internships to high school students and
work with local schools to update their curricula. All this—excellent initiatives worth noting, but much more will be needed; not only for social cohesion but also to ensure the diversity and flexibility of tomorrow's workforce.
