By Alfaritsy Artificial Intelligence (AI) is emerging as a transformative force comparable to previous General-Purpose Technologies (GPTs) that drove past industrial revolutions, with expectations of bringing about even greater changes in Africa.
Brad Smith, the co-chair and president of Microsoft, emphasized the significance of this technology when he stated, “Throughout history, technological advancements have reshaped economies,” as he elucidated.
He states that the initial Industrial Revolution was fueled by advancements in ironworking, the subsequent one by electrification and machinery, and the most recent one—which has unfolded over the past five decades—by the advent of microchips and software.
Smith further notes, “Every time we witness a new Industrial Revolution, everyone seems preoccupied with identifying the key leader.” However, he emphasizes that historical evidence shows the nations which make the greatest strides are typically those where the technology becomes broadly disseminated and embraced.
Since technology transforms all aspects of an economy, the nation utilizing it extensively will reap the greatest benefits.
This can be clearly seen in the case of electricity: gross domestic product (GDP) grows as electricity is adopted and used. “There is no country where this is the exception,” Smith states. “And the same is true in Africa.”
This indicates that the secret to achieving future success lies in implementing versatile technologies, he notes.
In this emerging AI age, we begin with the firm belief that it represents the next evolution of GPT.
According to Smith, countries aiming to successfully implement a GPT must excel in four areas.
The first is the technology itself. “It turns out that every GPT is built on a technology stack. This creates a new economy since every layer of the technology stack matches up with new companies, new types of firms and new types of jobs
Today’s AI technology stack consists of three layers: infrastructure, platform and applications.
“The IS layer is massive,” Smith says. “It requires the billions of rands that we are investing in data centres. Just as you can’t have electricity without power plants, you can’t have AI without data centres.”
The platform layer consists of the open source or closed source models that are created, to harness data and train models. “This is where a new generation of AI platform and software services are being created.”
At the top of the stack is the application layer, which is where people can get things done.
“With electricity, it was in the appliance layer that the magic happened, and the same is true today with the AI technology stack, where applications are what people see and use,” Smith says.
To establish a new AI-driven economy, you must have all three elements working together to kickstart the cycle. The infrastructure enables the development of models, which permits individuals to create applications; subsequently, this leads to further expansion of the infrastructure.
He highlights the South African Revenue Services (SARS) as a premier instance of an organization employing cutting-edge technology, including artificial intelligence, for tax collection purposes and transforming citizen interactions with governmental bodies.
When constructing a new economy, both governments and businesses must go beyond mastering the technology. As Smith highlights, they also need to have a strong grasp of economics.
The economics of artificial intelligence revolves around economic structures, financial frameworks, and business models.
For instance, he points out that each technology stack includes an economic framework, where components at the base tend to be costly, whereas those at the peak are relatively inexpensive.
“It is the same with AI: you see billions of dollars invested at the infrastructure layer; and find students can start to build new companies relatively cheaply at the applications layer.”
Next, it’s necessary to build a financial architecture. Smith explains that companies like Microsoft are working with partners to finance the construction of AI infrastructure around the world.
This usually begins with private companies and private funds, followed by sovereign wealth and additional public financing. This framework isn’t solely aimed at releasing capital; it also focuses on stimulating demand.
When nations struggle to gather the necessary amount of capital, Smith suggests they might consider forming partnerships within geographical regions. “An alternative approach to speed this up is through the utilization by the public sector.”
The government plays a vital role in kickstarting this process, not just through policy formulation but also by ensuring that all aspects of the public sector adopt the technology.
Developing the appropriate business models represents the ultimate, and essential, stage for achieving sustainable success. As Smith highlights, “We can gain insight from history that these business models tend to evolve.”
The third key to capitalizing on the emerging AI economy involves developing skills.
This is simply commonsense,” argues Smith. “If it’s crucial for something to be utilized across the entire economy, then the necessary skills should also be widespread throughout the economic landscape.
The previous Industrial Revolutions addressed this with technical institutes and apprenticeships in the first; land grant universities and industry standards in the second; and employee training coupled with computer science in higher education in the third.
When scaling up workforce development for the AI age, Smith argues that we require three categories of skills. First is AI literacy, enabling individuals to leverage these technologies effectively in their tasks. Next comes AI engineering, an extension of traditional computer science disciplines. Lastly, he emphasizes the importance of AI system design, aimed at empowering non-technical professionals with the ability to integrate AI into strategic planning, operational procedures, and process improvements within businesses.
“Each nation requires a comprehensive AI talent plan to evaluate market demands and increase AI literacy,” according to Smith.
This would cover more than just universities and technikons; it should also incorporate programs such as South Africa’s Youth Employment Scheme (YES), aimed at enhancing wider skill development.
According to Smith, for a technology to be widely adopted, it needs to achieve a certain degree of societal acceptance.
And for a technology to gain social acceptance, it needs to be both beneficial and reliable.
The development of a genuine strategy and framework of trust is essential for the future of AI.
To build this trust we need to have privacy, digital safety and responsible AI, he adds.
“This is why we are building AI governance from the ground up, from internal company policy to global policy.”
According to Smith, sustainability is a crucial component of the trust formula. He emphasizes that “we cannot adopt a responsible stance towards AI without tackling the necessity for sustainable AI.”
To turn the AI economy into a tangible reality, businesses and nations must concentrate on every aspect of it. “This is both incredible, exhilarating, and intimidating,” Smith remarks.
He emphasizes that the key to accomplishing everything lies in being inspired. “Consider all the incredible possibilities we have with AI.”
“But, while we should be inspired by everything that can go right, we should draw equal inspiration from everything that can go wrong.”
As an example, he points out that it is now 140 years since Thomas Edison lit up lower Manhattan with electricity. And yet, today, most of the population of Africa has no access to electricity.
“This is a cautionary tale that should inspire is to do better,” Smith says.
“We have the opportunity to dream bigger, be bolder and pursue something that Edison could never have dreamed of doing.”
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