Nvidia CEO Jensen Huang recently made a controversial statement at the World Government Summit in Dubai, suggesting that the need for individuals to learn how to code is diminishing due to advancements in artificial intelligence. Huang argued that with AI taking over programming tasks, it's no longer necessary for young people to learn computer science or programming. Instead, he believes that the focus should shift towards developing expertise in fields like biology, education, manufacturing, and farming. Huang's perspective is that AI will make everyone a programmer, as it will replace the need for traditional programming languages with human language prompts, making coding accessible to everyone.

Should we just disregard what he said?

Present Scenario

Currently, trained models are marketed as AI, though the original intent of AI was to augment or replace human intelligence more effectively. If that becomes a reality someday, then the statement about coding's demise (not only) gains weight, especially for those whose livelihoods depend on coding.

A Historical Lens

The discourse on technological unemployment isn't new.

In 1930, British economist John Maynard Keynes warned of technological unemployment with his writing . His insights highlighted concerns about labor-saving advances outpacing the creation of new jobs.

Karl T. Compton, the president of MIT from 1930 to 1948, contributed significantly to the discussion on the impact of technology on employment, particularly in his 1938 article titled "The Bogey of Technological Unemployment." Compton questioned whether machines would serve humanity or become destructive forces, framing the debate over jobs and technological progress.

Over time, we've witnessed the gradual replacement of human physical labor, particularly tasks not requiring constant decision-making, with machinery. This transition has led to higher precision and effectiveness at reduced costs.

Compton's perspective remains relevant today, especially in the context of contemporary fears surrounding Artificial Intelligence (AI) and automation, as it emphasizes the importance of understanding the broader economic impacts of technological change.

Envisioning a Jobless Future

Imagine a future where:

• Life is primarily about living to the fullest.
• Jobs don't define one's status.
• People aren't forced to work for survival.
• Soldiers have choices beyond just "Kill or Be Killed."

However, we're not there yet. Until then, we'll adapt to changes as they unfold.

The Real Problem: Slow Pace of Adaptation

While envisioning a future where AI reshapes the job market may seem daunting, the true challenge lies in the gradual pace of adaptation. Unlike sudden upheavals or economic crises that demand immediate attention, the slow erosion of traditional job roles due to AI may go unnoticed by authorities and the masses alike.

The incremental nature of this transition can lull individuals into a false sense of security. As long as there are still opportunities for employment, albeit dwindling, many may cling to the hope of securing a livelihood through traditional means. This reluctance to acknowledge the shifting landscape of employment can result in complacency among both policymakers and the workforce.

The consequence of this gradual adaptation is a creeping sense of insecurity and fear of the future. Without a clear understanding of the long-term implications of AI on the job market, individuals may find themselves ill-prepared to navigate the changing dynamics of employment.

Contrastingly, urgency in adaptation is paramount. If the transformation were to occur suddenly and dramatically, it would likely garner immediate attention from authorities and prompt decisive action. The urgency of the situation would compel both policymakers and the workforce to focus on strategies for adaptation and mitigation.

By highlighting the real problem of the slow pace of adaptation, we underscore the importance of proactive measures to address the challenges posed by AI-driven automation. Only by recognizing the gradual erosion of traditional job roles can we begin to formulate effective strategies to ensure a smooth transition for workers into new, AI-driven industries.

Should We Still Learn to Code?

Absolutely. Even if coding becomes less of a profession in the future, retaining the ability to code remains vital.

Just like many subjects in our academic curriculum aren't directly tied to specific professions but are still deemed essential, coding, which has shaped our digital future, should be no exception.

Ability will grant us a sense of control and independence in a dependent future.