The Illusion of Intelligence: Own, Acquired & Artificial

As we engage with new forms of intelligence, perhaps mistakenly called artificial intelligence, we're missing a fundamental insight: what we call "our own intelligence" has been artificially constructed for millennia. The fear of AI isn't really about machines becoming intelligent, it's about recognizing that human intelligence itself is largely a product of external influences, accumulated knowledge, and inherited patterns. This realization changes everything. It even gives more strength to know that by understanding this, own intelligence has always excelled.

The Uncomfortable Mirror

When we examine what we proudly call "our own intelligence," we discover it's constructed from the same basic building blocks as AI: accumulated data, pattern recognition, and learned responses. The difference isn't in kind, it's in transparency and speed.

While the term "Artificial Intelligence" might be misleading (perhaps "Assistive Intelligence" would be more accurate), the real issue isn't the naming, it's our reluctance to acknowledge that human intelligence has always been, in large measure, artificial.

The Illusion of "Own" Intelligence

Here's an uncomfortable truth: what we proudly call our "own intelligence" is largely not our own at all. It's a sophisticated amalgamation of:

• What we've read from books, articles, and research written by others
• What we've heard from teachers, mentors, colleagues, and conversations
• What we've experienced through events that countless others have lived before us
• What we've inherited from cultural, organizational, and educational systems

In the corporate world, this becomes even more pronounced. Our professional intelligence is heavily influenced by:

The irony is profound: we fear AI because it learns from existing data and patterns, yet our own intelligence is constructed in remarkably similar ways. The difference isn't in the fundamental process it's in the speed, scale, and transparency of that process.

Two Paths: Intelligence Amplification vs Intelligence Dependence

Throughout history, individuals and organizations have taken two distinct approaches to leveraging Assistive intelligence (in its broad sense): some use it to create new value, others become dependent and fail to transcend existing patterns.

The Intelligence Amplifiers: Creating New Value

Apple: Steve Jobs didn't invent the computer, smartphone, or tablet. He synthesized existing technologies with design thinking and user experience insights to create entirely new categories. Apple's intelligence was artificial—built from existing knowledge—but used to generate unprecedented value.

Amazon: Jeff Bezos combined existing retail, logistics, and technology intelligence to reimagine commerce. AWS emerged from Amazon's internal infrastructure intelligence, creating the cloud computing industry by recognizing patterns others missed.

Netflix: Reed Hastings took existing entertainment and technology intelligence, combined it with data analytics, and transformed how content is created, distributed, and consumed globally.

The Intelligence Dependents: Failures of Transcendence

Kodak: Despite inventing the digital camera, Kodak remained trapped by the intelligence patterns of film photography. They had all the assistive intelligence needed to lead digital transformation but failed to synthesize it into new business models.

BlackBerry: Dominated mobile email by perfectly executing existing communication intelligence patterns. When the iPhone redefined mobile intelligence around apps and touch interfaces, BlackBerry couldn't transcend their inherited assumptions about mobile devices.

Credit Suisse: Accumulated centuries of banking intelligence and regulatory knowledge, yet failed to synthesize this into sustainable value creation. Their intelligence became a liability when it prevented adaptation to new financial realities and risk management paradigms.

Lessons from the Field

Throughout my career transforming core banking systems, payments platforms, and cloud architectures, I've observed that the most successful projects synthesized existing regulatory knowledge, technical patterns, and business intelligence into new solutions rather than simply implementing best practices. When we migrated 25 banking clients across datacenters, modernized ISO 20022 payments processing and renovated Trade processing systems, success came from questioning inherited assumptions about how things "should" work and creating hybrid approaches that transcended traditional boundaries. The projects that failed were those that treated accumulated industry intelligence as gospel rather than raw material for innovation.

The organizations that thrive with AI are those that view it as Assistive Intelligence a powerful tool that amplifies human judgment, accelerates human analysis, and automates human routine work, but never replaces human wisdom, creativity, or accountability.

Embracing the Assistive Intelligence Revolution

To move towards productive adoption, we need to reframe our relationship with AI:

From Perfection to Iteration

AI doesn't need to be perfect to be valuable. Like our own intelligence, it needs to be useful, improvable, and properly guided.

From Fear to Curiosity

The same curiosity that drives human learning should drive our approach to AI. What can it teach us? How can it help us see patterns we've missed? What new possibilities does it unlock?

The Path Forward

As someone who has navigated multiple technological transformations from mainframe to distributed systems, from on-premises to multi-cloud, from monolithic to microservices I see AI as the next logical step in human-technology collaboration.

The key is view Artificial Intelligence as a as natural evolution. Just as we augment our physical capabilities with humans, tools and machines, we can augment our cognitive capabilities with AI systems that learn, analyze, and assist.

In embracing this partnership, we'll discover that the line between "artificial" and "natural" intelligence was never as clear as we thought. After all, all intelligence human or otherwise is built on learning from what came before. And alawys human intelligence could create new value by synthesizing existing knowledge in novel ways.