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Reinvention in the age of AI

Change is inevitable. Heraclitus, a Greek philosopher born in 544 B.C., said, “You never step in the same river twice, for it’s not the same river and you’re not the same person.” Humanity has been striving to reshape the world for the better since the beginning of time. The earliest tools mankind invented were shaped out of stone. The latest ones are shaped from technology. Every innovation we create seems to inspire twin currents of fascination and fear — fascination with how it will advance our society and fear that it will render us obsolete. Consider these examples from a recent World Bank report:

  • In the late 1500s, Queen Elizabeth I refused to grant a patent to the inventor of the knitting machine, fearing it would deprive her subjects of work. It was the first major stage in the mechanization of the textile industry which later led to the Industrial Revolution.
  • In the 1880s, the Qing dynasty fiercely opposed constructing railways in China, arguing that the loss of luggage-carrying jobs might lead to social turmoil. Today, trains in China move 54% of domestic trade by train, more than any other major country.
  • In 2018, robot density per worker was the highest in Germany, Korea and Singapore. Yet the employment rate in all of those countries remains high despite the prevalence of robots.

The only constant is reinvention

In the economic game of survival of the fittest, reinvention is a constant theme. Where would Samsung be today if it still sold dried fish? True reinvention impacts both an industry’s businesses as well as its individual participants. For example, Uber is often mentioned as an example of an industry disruptor that displaced jobs for incumbent taxi drivers. However, a recent analysis of Uber’s impact on U.S. cities showed that Uber not only increased the number of jobs for drivers by 50% on average, but the wages for Uber drivers were about 10% higher.

The same is true for technological progress — while it does disrupt the way things were done in the past, it also opens the door for new opportunities and skills. For example, instead of hiring traditional loan officers, a leading fintech platform in China created more than 3,000 data analysis jobs to sharpen algorithms for digitized lending, according to the World Bank Group report. In the manufacturing sector, GE is advancing its own digital transformation by enabling employees to learn the skills needed for future jobs through its “Brilliant Learning” program. In fact, ManpowerGroup reported that 87% of employers across 44 countries plan to upskill their workforce to fill talent gaps. And, for those willing to take the reinvention plunge, there’s a rich array of free education on AI from universities, corporations and massive open online courses, such as Udemy, Coursera and edX.

Augmenting human ability

While innovation often does require new skills, its primary goal is to augment human capabilities, enabling us to do bigger and better things. AI promises to do the same in this era of data explosion, where Cisco predicted that every person will generate 50 GB of traffic per month by 2022. That’s 10,000 times more data than the first hard disk held in 1956. While that seems like a giant leap, it’s not surprising given the proliferation of devices connecting to IoT, the latest contributor to the daily deluge of data. It’s simply not possible for the human mind to process this amount of data. So tasks that depend heavily on processing large amounts of information to achieve better outcomes will require AI help — for example, calculating insurance or financial risks, diagnosing diseases, detecting and pinpointing danger, identifying ideal job candidates and so on. The best outcomes, however, will come from AI+human collaboration as a recent experiment demonstrates. In the test, a clinical AI was pitted against human doctors on a medical exam, and while the AI outperformed the human doctors, the AI+human group performed best of all.

The power of platforms and crowds

Interconnected digital platforms are the underlying foundation of the information economy and AI+human collaboration. Today, virtually anyone with an internet connection can learn and develop new skills, start a company, trade goods and services, crowdsource app testing or solutions to complex problems and even build their own AI through digital platforms. By connecting customers, producers and providers in a one-to-many fashion, these digital platforms have become essential hubs of collaboration and innovation with their own inherent value. In a recent talk, Andrew McAfee, co-author of “Machine, Platform, Crowd: Harnessing our Digital Future,” shared an illustration of how quickly an ecosystem can develop its own value. He said when Apple first opened its app store to external developers, there were over 10 million downloads of those 552 apps in the first few days.

Since then, businesses have come to recognize the market value of these digital ecosystems as their customers increasingly choose products based on the services, content and intelligence they can provide. The market value of a smart home device, for example, is largely determined by the digital services its IoT ecosystem of connected providers can deliver. In many cases, embedded AI intelligence also enables the device to deliver a hyper-personalized experience of those services by learning and adapting to user preferences, voice, behavior and more. These smart devices, whether in the home or enterprise, are returning a treasure trove of insights on user preferences, needs and consumption behaviors that are creating new markets in areas like IoT data exchanges.

Better together

Society evolves and rebalances every time innovation occurs, and, despite our fears, the reality is that today a greater portion of humanity has access to better jobs, education and healthcare than ever before. Interconnected ecosystems are key enablers for these innovations and AI+human collaboration. Just as previous technological progress created new jobs that didn’t exist before, advancements in AI will create new jobs in app development, edge device operations, AI ethics compliance, data science and more. Broadly speaking, new opportunities will arise in these areas — framing the problems for AI to solve by providing contextual awareness, training algorithms, ensuring ethics compliance and responding to AI advice or output. And when the suggestions include new patterns or questions that hadn’t been thought of before, it will take human imagination to explore the uncharted waters and discover blue oceans of new markets.

All IoT Agenda network contributors are responsible for the content and accuracy of their posts. Opinions are of the writers and do not necessarily convey the thoughts of IoT Agenda.

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