Risk Of Foundational Knowledge Beyond 2026

By Victor Kuarsingh

AI is pushing the boundaries of technological abstraction further than ever before. While no one is arguing we should abandon modern tooling and code like it’s 1965, this rapid evolution introduces a silent organizational risk. Every previous era of computing has shown that when an abstraction breaks, the organizations that are impacted most are the ones where technology creates problems and few or nobody is left in the building can reason about what is underneath.

The Challenge Ahead

When we look back over the past 60-70 years, the industry has come a long way from our early computing and programming era.  In those early days, the application user was most often the code developer with a necessarily deep understanding of the foundational computing system by which functions were built.   As operating environments evolved, such as the release of Unix by AT&T Bell Labs in the 1970s, programmers and users were able to leverage a more abstracted environment resulting in productivity gains. As we approached the 80s, that further evolved where end users were rarely the programmer, and even more unlikely to have a deep understanding of the foundational computing platforms. 

In the 1980s, the acceleration of networking was driven by the need to expand the early ARPANet that bloomed into the Internet by the mid 1990s.  Those early networking experts were most often computer scientists that formed early connectivity protocols and systems morphing into a defined discipline by the mid-1990s.  Early networking talent, just like the early systems and programming talent, was often very well versed in the foundational infrastructure’s base architecture, design and capabilities. Moving further into the 2000s, the rapid expansion of vendor supplied products, standard deployment models, and further abstraction began to distance users, administartors and programmers of networking systems from those foundational capabilities. 

The advantage of these abstractions both within the software and networking space were revolutionary in allowing for scaling and acceleration of deployments, modern applications and outcomes.  However, an artifact of this change is the slow and often silent deterioration of foundational expertise in many businesses. Areas where experience often now lacks includes networking, security, computing, operating systems and platforms.  The deep learning curve that was needed to be functional years ago, no longer is mandatory for most designers and engineers to operate within their areas of the business.  That separation and devolution of “making it work” contrasting to understanding “how it works” – poses a risk to business, especially when those foundations are needed for resiliency and overall business success.

AI, which has displaced so many foundational tasks and roles, not only further adds to the separation, but may further accelerate the deterioration of skills development and growth of cognitive reasoning when designing, using and improving systems or platforms.  The new risk somewhat exacerbated by AI is the release of engineering rigor as a pillar of development teams are able to produce without really knowing how the product (code or config) is being generated.   

With a declining population of highly competent engineers that understand the foundations, and an ever increasing demand for those people, its going to be interesting to see what transpires in the market and in businesses.  Early impacts will likely be filled with outages and missed opportunities for businesses.  The longer term impact may be foundationally  anemic businesses unable to produce substantive products and services relegated to producing fragmented and unreliable technical products.   The challenge we face as business leaders is – can we leverage AI to accelerate now without sacrificing our future success.

Modern examples where eroded engineering rigor and abstraction-layer failures caused significant business impact include CrowdStrike (2024), Meta (2021), and Knight Capital (2012) — each resulting in substantial financial loss and reputational harm.

So What

The challenge we face as leaders and within the Industry is clear.  We must leverage AI to accelerate our velocity without sacrificing our future integrity.  We need deliberate balancing between embracing this new layer of abstraction while still protecting our base of needed expertise. 

Allowing AI to completely sever our engineering teams from the underlying mechanics of our networks, systems, and platforms introduces a business risk. When a catastrophic failure inevitably occurs beneath the AI-generated layer, the absence of deep, cognitive reasoning won’t just cause a routine outage—it could result in an entirely unrecoverable situation where no one in the building knows how to rebuild the collapsed foundation.

AI can generate the code and configure the platforms, it cannot assume the risk. Moving forward, engineering rigor cannot be outsourced. We must intentionally cultivate, value, and retain the talent that understands the “how,” ensuring that as our technology becomes increasingly abstracted, our ability to govern, troubleshoot, and recover our businesses remains firmly intact.