The AI revolution: Sowing seeds or dust?

There is a déjà vû sense today, ten years after the Great Recession began in 2007. Very much like in 1939 when the 20th Century's deepest, widest depression ended, today's society is waking up looking different because everything around them has been (or is being) so structurally transformed. That change has been so messy, they have not been able to live their personal lives as before.

In the 1930s it was not the world war that was about to begin that catalysed the change, but the beginning of the evaporation of agrarian domination in the US economy (owing to the advent of technologically advanced inputs). With that came the start of the urban migration that would dominate the 20th Century landscape. Metropolitan growth, as for example in the United States, depended upon and encouraged manufacturing plants, which gobbled up those migrants, opened world markets, and bred all sorts of services to supervise unprecedented levels of production and flux.

Today's catalysts, on the other hand, happen to be a combination of the unpredictable, even low-intensity wars lingering on many fronts, and artificial intelligence (AI) displacing human labourers, thus reconfiguring our lifestyles as dramatically as those 1930s machines and the idea of mass-production. For example, AI cuts into education and investments while promising automated or augmented solutions to everyday problems. Paradoxically, AI also reinforces national boundaries, even as travel, tourism, and technologies have penetrated more countries than ever before, while boosting production levels. Factory and service employees not only face robots, drones, and other man-made contraptions as substitutes, but also find them outperforming humans precisely where humans thought they had comparative advantage: in increasing production volumes, skills, and the speed needed to become competitive.

Both transformations have imposed economic, social, and political costs. A survey of 2017 bankruptcies sheds light on the nature of the reconfigured corporation structure (and implied villains). When we look at such names as American Apparel, Guess, Gymboree, The Limited, Payless, Radio Shack, Sears & Roebuck, Toys R Us, among many others, on the 2017 list of closures, we quickly conclude, since retailers bore the brunt of the pressures, they needed to be rehabilitated somehow. At least one valid reason why surfaces: Amazon and other dial-phone or online outfits bring customised orders to the client's doorsteps, saving time and hassles in an era when the typical US citizen diverts up to four hours on the phone/Internet every day, thus relishing the home-delivery services. No wonder the retail age peaked against today's technologies.

Glimpsing through Fortune 500, we find only 60 corporations, or merely 12 per cent, being on the same list in the 1950s, that is, after World War II recovery was complete. Further scrutiny, if intuition is apt, informs us a chunk of the 88 per cent that vanished were manufacturer-based, that is, those that took off or experienced their hey-days after the 1930s depression. That the '2007-10 Great Recession' did not leave the antiquated industries untouched is evident from comparing the Top-five technologically-driven corporations in 2007 and 2017. Petrochina, ExxonMobil, General Electric, China Mobile, and the Bank of China were the 2007 leaders, in that order, while Apple, Alphabet, Microsoft, Facebook, and Amazon rule today, in that order.

Whereas Petrochina and ExxonMobil were extractive industries, General Electric a manufacturing-cum-retailing, China Mobile in the software sector, and Bank of China in the services, in 2017 all the top five corporations happen to be in the information industry, playing with software, investing in technology, changing outputs so constantly that a glued customer-base lives perpetually on the verge of insolvency, and slowly substituting the original source of intelligence, that is, humans, with artificial counterparts. We can even deduce these five will also not remain at the helm for long unless they constantly diversify, and thereby monopolise as many sectors as is possible. Most importantly, the 2007 Top-five employed almost twice as many more people than the 2017 Top-five do, a diminishing function that will stalk us for the rest of the century when the need for escalating skills will be expanding.

All may not be that bad though. As Nicholas Rapp and Brian O'Keefe informed the World Economic Forum audiences in January 2018, and through them the rest of the world, the ongoing AI revolution is set to expand global gross domestic product (GDP) by $15.7 trillion in another decade or so (see https://www.weforum.org/agenda/2018/01/these-100-companies-are-leading-the-world-in-artificial-intelligence?utm_ content=buffer 31a8f&utm_ medium). Though 42 per cent of them operated in the United States and 23 per cent in China in 2017, the 2,452 AI corporations listed in 2017 engage in servicing efficient and innovative production in a wide variety of sectors. Led by the $3.1 billion-funded Bytedance in China, they engage in News and Media (Bytedance), Humanoid Robotic (Ubtech company) within the robotic and AI hardware stream; Voice Recognition (Mobvoi, in China), and Autonomous Vehicles (Zoox, in California), within the Internet of Things and Automobile stream; Computer Vision (Floored, in New York) within Cross-Industry and Other Categories stream; and so forth. Just by surveying the other streams, we can get a sense of how diversified and ubiquitous the AI start-ups have become, and why there should be deep fear for anyone holding on to a regular job today. Those streams include Cyber and Physical Security; Risk and Regulatory Compliance as well as Legal Technicalities; Science; Leisure; Fintech and Insurance; and so on. These streams (and corporations) can all be found in the Top-hundred of the AI industry, serving as the engine of the global GDP expansion headed our way.

It goes without saying the benefits will not be symmetrically distributed. In fact, with jobs being so extensively and deeply slashed, this current economic transformation may hurt a far higher of the national or global population than ever before. Here it gets tricky. To sustain the systems producing the much-needed outputs today (a cell, with all its access available as widely as possible, Internet infrastructures making door-to-door customer services easier, leisure sectors to cater to the growth in travel and luxury lifestyles, and so forth), customers/clients become more desperately depended on corporate survival. Factories and assembly-lines can be protected by the government to flourish, as they have been since the 3rd Industrial Revolution began in the 1930s, but safeguarding software to earn them profits may take the government far more into a person's life to be acceptable and successful within a democratic atmosphere. A can of worms is opened, since identifying which software to protect and which to leave unfettered opens a slippery slope bound to damage industry competitiveness over the long haul.

Ultimately, whether we learn the much-needed software or not is a personal choice, not the government's, and the 4th Industrial Revolution's Achilles Heel might lie right here: the make-it or break-it juncture in diverting the booming Internet and cell-phone subscribers from their own online engagements towards company production (like professors desperately strive to retain student attention from the constantly buzzing mobile or tweets in class these days). Since the declining US and ascendant Chinese competitive edge positions the two countries on different sides along this very spectrum, it becomes obvious how US-type freedom invites diversionary activities, which China-type discipline reins in. How the future unravels will be determined by how these competitive dynamics get juggled, particularly in these two countries.

Dr. Imtiaz A. Hussain is Professor & Head of the newly-built Department of Global Studies & Governance at Independent University, Bangladesh.

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