Artificial Wisdom, Not Artificial Intelligence
By Henri Fouda, PhD. CEO of Amerafric Capital
During 2025, as financial services leaders accelerated the integration of increasingly advanced AI models into their operations, I reflected on the concept of the “bekon” from my mother tongue, Ewondo. In Beti culture, the bekon represent ancestral figures actively engaged in offering blessings and guidance. They symbolize collective wisdom—having observed various patterns and cycles and transforming experience into advice. Nevertheless, it is the responsibility of the current generation to address contemporary challenges, which demand adaptive intelligence and the capacity to manage unprecedented circumstances.
This distinction, I believe, clarifies what we’ve actually built with large language model; and why the industry has been asking itself the wrong questions.
Approximately thirty years ago, during my time as a graduate student, I experimented with early neural network models designed to predict market trends. Although these systems were rudimentary by contemporary standards, they provided a critical insight: even advanced pattern recognition does not equate to true intelligence when confronted with unprecedented scenarios. The Long-Term Capital Management collapse in 1998 starkly illustrated this point, as models trained on historical data failed dramatically when faced with conditions beyond their experience.
Today’s LLMs are exponentially more sophisticated, yet they operate on the same fundamental principle: learning patterns from accumulated data. We’ve been calling this “artificial intelligence,” but perhaps we’ve misnamed our creation. What we’ve built is closer to artificial wisdom, that is, systems that excel at synthesizing humanity’s accumulated knowledge, recognizing patterns across vast textual territories, offering contextual guidance drawn from billions of human expressions.
The Language Game: An Ongoing Challenge
Wittgenstein demonstrated that meaning arises through usage and the dynamic interaction of language within real-world contexts. Large language models (LLMs) process established linguistic patterns rather than contribute to the generative creation of meaning as it occurs in new situations. In this regard, LLMs consistently remain one step removed from the active participation in meaning-making that characterizes human engagement in language. This is not a defect; rather, it represents a categorical distinction. For instance, currently:
With increased processing power, AI now identifies market trends that people often overlook. However, in 2025, global markets faced dramatic changes: U.S. stocks lost their dominance, precious metals experienced an extraordinary surge, and volatility rose sharply due to new geopolitical developments, innovative fiscal and monetary strategies, and shifts in global trade; each arising from circumstances not seen before. To address these complexities requires more than advanced pattern recognition; it calls for thoughtful reasoning about structural changes and the ability to manage new combinations of variables that can have major impacts.
Within legal reasoning, AI systems demonstrate proficiency in locating precedents, they identify pertinent case law and synthesize established doctrine; functions rooted in classical expertise. However, when faced with conflicting precedents, novel regulatory challenges posed by emerging technologies like AI itself, or circumstances requiring innovative solutions over consistency, judgment demands genuine intelligence: the ability to adapt to authentic novelty.
In medicine, diagnostic AI systems show continual improvement, trained on ever-larger datasets. However, clinicians deliver breakthrough responses to emerging health challenges by applying adaptive, embodied reasoning. They make decisions with incomplete information, face real consequences, and bear the irreducible weight of responsibility that arises from care of actual patients rather than interpretation of patterns.
The Distinction Matters
Decisions of significant importance are being made based on a misunderstanding. The ongoing concern that artificial intelligence may “replace knowledge workers” confuses wisdom with intelligence. While AI demonstrates proficiency in information synthesis, pattern recognition, interpolation and the application of established frameworks, it remains unable to exercise judgment in entirely new scenarios, make ethical decisions with substantial consequences, or demonstrate creative adaptability in unprecedented circumstances.
This misframing presents two significant risks. First, it leads to the automation of decisions that necessitate human intelligence, particularly in situations where pattern-matching proves inadequate due to substantive differences from past precedents. Second, it may result in the deterioration of human cognitive abilities by regarding them as superfluous in comparison to the interpretative capacities attributed to AI.
The productivity paradox of 2025, characterized by sustained significant AI investment alongside limited overall productivity improvements, may be attributed to a misalignment in system application. Specifically, wisdom systems have been implemented in areas where intelligence is required, which may explain the lack of transformative outcomes.
A Better Framework Forward
Recognizing AI as artificial wisdom rather than artificial intelligence suggests different priorities for 2026 and beyond:
Build better wisdom systems: more accurate, less biased, more transparent about limitations; but stop pursuing “artificial general intelligence” as if it were merely a scaling problem. Intelligence in its truest sense may require embodiment, mortality, pain, consequence, that is, things silicon systems fundamentally lack.
Redesign human roles to emphasize what cannot be delegated: judgment in novel situations, ethical reasoning in ambiguous contexts, adaptive creativity when circumstances demand questioning received wisdom. If AI excels at retrieving wisdom, humans should prioritize development of their intelligence.
Structure human-AI collaboration as partnership: artificial wisdom serving living intelligence, vast knowledge serving human judgment.
Calling Upon the Bekon
According to Beti tradition, the bekon are invoked not for their direct intervention in contemporary issues, but for the valuable context their accumulated experiences offer in informing our judicious decisions. While they impart wisdom, it is incumbent upon us to exercise intelligence. The bekon remain engaged actors in our community specifically through our deliberate interaction with them, that is, an engagement that itself exemplifies the application of living intelligence.
The relationship between accumulated knowledge and adaptive response has been longstanding. The advancement of AI in 2025 may offer the valuable opportunity to distinguish clearly between these concepts, enabling the development of systems that respect both wisdom and intelligence while maintaining their respective roles.
The relevant consideration for 2026 is not solely the extent to which artificial intelligence can be enhanced, but rather how to develop systems of wisdom that support and enhance human intelligence, rather than diminish it. This represents a more constructive and sincere approach, that is, one that arguably merits greater attention in ongoing discourse.
