Technology as a second language. The new pre-requisite for thriving in the digital transformation era.
Why becoming well-versed in technology is the new socio-economic lubricant for upward mobility, and why today’s youth are more poised than ever to create a more inclusive future.
Increasingly, it is digital-first interactions that are shaping the evolution and constraints of how downstream human-to-human interactions unfold.
The global landscape has evolved. A paradigm shift in thinking is required to embrace the notion of how digitally-first interactions are shaping the lens and outcomes of human-centric interactions and workstreams.
In this era of digitalization and information, society is undergoing rapid transformation at a speed of change that arguably hasn’t been seen since the Industrial Revolution. An era where the release of transformer models, paired with raid advances in computing power, and the curation of large datasets (i.e. the internet and search data), has provided the fertile grounds for terms such as artificial intelligence and inclusive technology being not just buzz phrases, but a reality for operating in this new environment.
Today, the technology sector’s contribution to economic output surpasses its contribution to local employment (5.2% of economic output globally, but only 2.7% of employment).
To some, this may seem as if we are living in precarious times; an era wrought with rising socio-economic inequality, escalating geopolitical tensions, and mass migration at levels not observed since World War II. Where emerging technologies, socio-economic phenomena’s such as remote work, and evolving societal values, serving as the gasoline shaping this evolution from human-first decision making, to digitally-first becoming the default mental mode shaping the lens in how ensuing human interactions unfold.
Professor John Van Reenen, Director of the London School of Economics’ Centre for Economic Performance argues that the winnowing out of the middle class across OECD nations is in part due to technological changes that have tended to disproportionately benefit the highest-skilled or those already in the upper-echelons of society.
The proliferation of advanced transformer models has significantly intensified the digital transformation across various sectors. These models, now seamlessly integrated into widely accessible consumer applications, dramatically reducing the friction associated with traditionally human-centric tasks such as information gathering, data and content creation, summarization, and analysis.
A compelling illustration of this rapid adoption is OpenAI's release of ChatGPT in November 2022. Operating as a model-as-a-service, ChatGPT garnered over 1 million users within just five days of its launch and escalated to an unprecedented 100 million Monthly Active Users (MAU) within two months. This achievement made ChatGPT the fastest-growing consumer internet application in history. To provide context, popular platforms like TikTok took approximately nine months to reach the same milestone, while Instagram required about two and a half years.
And so it may seem to the youth of today that the future is bleak. According to the US Federal Reserve Bank, Millennials (those born between 1982 and 2000) face tougher borrowing rules, rising home prices, and lower income mobility, becoming the first generation to have family wealth that is 34% below what earlier generations held at the same time.
To thrive in the new economy, youth will need to migrate from becoming digital natives to digitally literate.
While personified as digital natives, Millennials and Gen Z’s are not necessarily digitally literate. It is a common misnomer that being exposed to emerging technologies automatically equates to knowledge of how to best leverage these tools for value-creation. Being able to order a meal from a smartphone or scroll through content on social media does not equate to being digitally literate. As the average amount of time spent online edges ever upwards, to 24 hours a week on average in the U.S., the physical and digital worlds are morphing together. And so, learning computer (programming) languages for the digital world is akin to learning human languages in the physical world; each garners access and opportunities for socio-economic mobility. Human language fluency needs to be augmented with digital literacy, “technology as a second language” if you will.
So how can youth best position themselves to benefit in the new economy?
With great disruption comes great opportunity. And so as digital natives, today’s youth now need to now take the next step: becoming digitally literate. While learning code or how to build an algorithm may be the final step in understanding how websites, apps and games operate behind the scenes, there are simpler places to start: learning computational thinking.
As Webopedia puts it, “Computational thinking…focuses primarily on the big-picture process of abstract thinking used in developing computational programs rather than on the study of specific programming languages. As a result, it often serves as an introduction to more in-depth computer science courses.”
Developing problem-solving skills, fueled by pattern recognition and a structured approach, is the start to gaining computer language proficiency. By beginning to think computationally, the young people can begin to train their minds to take a complex problem, understand what the problem is, and develop possible solutions in a way that a computer, a human, or both can understand.
Akin to how human languages have a mastery continuum varying from basic to fluent, so too do computer programming languages. Thus, there is no need to worry as learning a language or mental mode doesn’t happen overnight. Those coding classes will be still be there when ready for the next challenge!
The emergence of generative AI and the increasing prevalence of digital solutions highlight the need for a growth mindset to mitigate the drawbacks linked to less human-centered problem-solving.
The accelerated integration of Generative AI into high-skilled workflows is raising concerns about potential cognitive debt and a decline in critical thinking among professionals. Research, particularly from 2025, highlights that while GenAI enhances efficiency, it can lead to:
Increased Cognitive Offloading: Studies, such as those from Microsoft Research and Carnegie Mellon University (Lee et al., 2025), indicate a negative correlation between frequent AI tool usage and critical thinking abilities, mediated by a greater tendency to offload cognitive effort to the AI. This can result in diminished skill for independent problem-solving and a long-term overreliance on the tool.
Reduced Relevant Cognitive Load: Findings from Polytechnique Insights (based on a June 2025 study) demonstrate that using GenAI can significantly reduce the intellectual effort required to transform information into knowledge. Brain activity measurements (EEG) showed reduced brain connectivity, and a high percentage of users were unable to recall content they had just generated with AI, pointing to a form of "cognitive atrophy."
Automation Bias and Uncritical Acceptance: Professionals, especially those with high confidence in AI's capabilities, are increasingly prone to automation bias, leading to less scrutiny and critical evaluation of AI-generated outputs. This is particularly true in "low-stakes" or routine tasks, where the absence of critical thinking can lead to the propagation of errors or biased information.
Shift in Skill Demands: Rather than entirely eliminating jobs, GenAI often redefines them, shifting the nature of critical thinking from direct problem-solving and idea generation to information verification, response integration, and task stewardship (Lee et al., 2025). While this can enhance productivity, it risks making employees "mere overseers," potentially leading to less diverse and less original outcomes.
This emergent trend suggests a potentially lower quality of human interactions, particularly in professional contexts where nuanced judgment, original thought, and deep analytical engagement are paramount. If professionals primarily operate in digitally-first work streams, where AI generates initial content and solutions, rather than human-first work streams that emphasize human ideation, critical analysis, and collaboration from the outset, the long-term impact on the depth of human insight and the richness of interpersonal professional exchange could be substantial. The challenge lies in designing and implementing AI strategies that augment human capabilities and foster critical engagement, rather than inadvertently eroding them. ###Note: The Summarization task of the key points ideated by author, Jennifer Vlasiu for generativeAI section was executed by Google Gemini##