Why Contextual Thinking Is the Future of Human Intelligence in the Age of AI

I was in Venice for a couple of days. Even if I had conducted extensive research beforehand, it was the personal touch of my Italian friend, Tommaso Tomaselli, traveling down from Vittorio Veneto to guide me around, that gave me the indispensable context to truly appreciate Venice. He took me up the Campanile di San Marco, offering a panoramic view that transformed my perception. Instantly, everything—the architectural miracle, the maritime heartbeat of the city, the neighboring islands like Murano, the expansive lagoon—became clearer. This elevated vantage point offered a dramatically different perspective than navigating Venice’s narrow streets or hopping from one bacaro to another.

As if this newfound context weren't enough, my attention was captured by a modest plaque dedicated to Galileo, mounted on one of the observatory’s walls. I realized I stood at the birthplace of modern science, the very spot where Galileo observed Jupiter’s moons, Venus’s phases, the moon’s rugged surface, and sunspots. Galileo fundamentally altered humanity’s worldview by shifting Earth from the universe’s center to orbiting the Sun. He thus emerged as one of history's greatest contextual thinkers—not because he invented the telescope (he didn’t), nor because he was the first to peer into space (he wasn’t), nor even because he uncovered heliocentrism (he didn’t). Rather, Galileo’s genius lay in synthesizing existing knowledge into empirical proofs, reframing understanding in ways accessible to all.

History is replete with contextual thinkers who profoundly advanced human civilization, often without inventing something entirely new. Charles Darwin reframed biology, placing life within the context of time through the slow mechanism of natural selection. Albert Einstein replaced Newtonian absolutes, revealing space-time’s flexible nature depending upon the observer. Sigmund Freud illuminated the unconscious, situating human behavior within hidden emotional realms. Alan Turing imagined thinking machines, setting the stage for the digital era. Mahatma Gandhi reconceived resistance by embedding political struggle in the framework of moral strength and nonviolence. These thinkers, like Galileo, connected, repurposed, and reframed existing knowledge, altering reality itself.

Contextual thinking by design is interdisciplinary in nature. It involves layering subjects one over another, creating a mesh to filter insights from existing information. Genuine breakthroughs demand new combinations of available knowledge. Tulip Mania, often portrayed merely as psychological herding or irrational exuberance, exemplifies contextual thinking. In the 17th century Dutch Golden Age, tulips transitioned from exotic botanical curiosity to a highly speculative asset, embodying everything speculative and irrational. However, deeper contextual analysis reveals it as a complex, chaotic informational system influenced by biological, social, and environmental realms. Tulips exhibited color-breaking due to a virus discovered by Dorothy Mary Cayley in 1928, transmitted by sap-sucking aphids. Ants maintained symbiotic relationships with aphids, protecting and "milking" them for honeydew, indirectly influencing the virus's spread. Economic conditions intertwined with the biological systems; the bubonic plague simultaneously dampened and amplified market speculation as societal responses shifted unpredictably. Thus, information operates within multiple interwoven realms, shaping human perceptions and market behaviors profoundly.

However, cultivating contextual thinking faces significant hurdles, chiefly our innate drive for instant gratification. Delayed gratification—a foundational element of contextual thinking—often loses to the seductive appeal of immediate rewards, creating an addiction to idiosyncratic behaviors. Humans are naturally drawn to idiosyncraticity because it signifies uniqueness, novelty, and exclusivity—qualities deeply appealing to our innate curiosity and desire for distinction. Consequently, 99.9% of consumed news revolves around idiosyncratic behavior, which offers immediate stimulation and easy consumption. Conversely, contextual insights typically reside in research realms, compelling the reader to think deeply, thus targeting parts of the brain less responsive to instant gratification.

Market structure often prevails over current human activity, discounting news and information generated by human events. For instance, despite Israel's engagements in Iran and earlier conflicts in Gaza, the Tel Aviv Stock Exchange continues to soar and outperform the S&P 500. This underscores how market structure regularly overrides content-driven interpretations, exemplifying the supremacy of context over content. The capital markets are replete with such examples.

Machines, extensions of the "wisdom of crowds," reflect collective mediocrity, following paths of least resistance and mediocrity. Although crowd wisdom predicts momentum and energy, outcomes remain unpredictable and subject to extremes—revolution or disaster. True innovation lies in independent thought or training machines to think divergently, expanding informational boundaries and counteracting collective biases.

How, then, can we foster contextual thinking or impart it to machines? Embracing history, interdisciplinarity, combinatory creativity, commonality, and incessantly questioning—"why?" and "why not?"—are critical. My book "End of Passive Investing" tackled the seemingly unbeatable nature of the S&P 500, exploring how systematic approaches could potentially outperform it, all by understanding context, how did MCAP happen, where did it come from, why is it biased, how the bias went undetected, did some one try to solve it, why did they fail etc.

The research, spanning 13 years, transcends mere investment solutions, delving into broader puzzles—next-generation search engines, datafication, quantum mechanics, psychological mechanisms, complexity redefinition, and evolving statistical laws—all rooted in daring contextual thinking and explored further in my forthcoming books.

In conclusion, imagining a world beyond Large Language Models (LLMs) is not difficult; indeed, humanity thrived intellectually long before computers. The essence of our intellectual pursuits remains unchanged—we are still juicing the information engine, pushing it towards eventual inefficiency. True intelligence does not emerge from endless data accumulation or computational brute force but through discerning, adaptive, and contextually resonant thought. Like the candle's subtle illumination contrasted with the blinding sun, real intelligence operates quietly, efficiently, and meaningfully. As we transition beyond mere predictive machines towards conceptual intelligence, we should build models that help us ask deeper questions rather than merely providing quicker answers. Ultimately, intelligence should not exhaust resources to appear wise but must sensitively reveal meaning. The future demands that we prioritize context, creating deeper mirrors, not louder echoes.