Key Takeaways
- Current generative AI and LLMs lack core wisdom attributes like compassion, self-reflection, and emotional regulation, necessitating the pursuit of artificial wisdom (AW) for meaningful advancement.
- Defining wisdom remains deeply contested – whether it requires embodiment, is separate from intelligence, or is recognizable only "in the eye of the beholder" – creating a fundamental hurdle for AW development.
- Research indicates AW would require new computational frameworks (e.g., mixture-of-experts architectures) modeling human psychosocial needs, not just scaling existing LLMs.
- Simple prompting can elicit AW-like responses, but establishing objective benchmarks to distinguish genuine AW from sophisticated mimicry is critical for measuring progress.
- AW may emerge organically from sufficiently complex existing AI systems as an unpredictable property, rather than requiring deliberate architectural invention.
The emerging discourse in AI laboratories signals a growing consensus that today’s generative AI and large language models (LLMs), despite their fluency, hit inherent limits in demonstrating true wisdom. As the article posits, "conventional AI doesn’t have what it takes to exhibit or express wisdom. Therefore, artificial wisdom must be invented." This isn’t merely academic; achieving AW could decisively shift the AI marketplace and accelerate the path toward artificial general intelligence (AGI). However, the journey is obstructed by profound disagreements on two foundational pillars: the very nature of human wisdom and whether an artificial counterpart could mirror it. Without resolving these, efforts to invent AW risk aimless duplication or misaligned goals.
Setting Up Generative AI and LLMs
Current LLMs like ChatGPT, Gemini, and Claude are built by scanning vast internet text to pattern-match human writing, storing this in artificial neural networks inspired by brain structure. Post-training, they undergo refinement via Reinforcement Learning from Human Feedback (RLMF), where human testers rate responses for traits like politeness, guiding the AI’s behavior through upvotes and downvotes. System-wide prompts further shape public-facing conduct. This process excels at statistical pattern replication but, critics argue, remains fundamentally detached from the nuanced, reflective, and affective dimensions associated with human wisdom.
The Fight Over Artificial Wisdom
A central controversy questions whether AI can possess wisdom at all. Detractors insist wisdom must be "embodied" – residing in human hearts, souls, and minds – rendering it impossible for disembodied machines. Proponents counter that past embodiment arguments (e.g., claiming AI couldn’t be empathetic) have been debunked by evidence showing users often perceive current AI as empathetic. As the article notes, "research studies have shown repeatedly that people using present-day AI are often of the belief that AI is as empathetic, if not more so, than humans that they know." This suggests the embodiment barrier might be surmountable through behavioral output alone, even without subjective experience.
The Battle About What Wisdom Is
Compounding the challenge is the absence of a universal wisdom definition. Some view it as subjective art – "you know it when you see it." Others tie it to sound decision-making and insightful judgment, yet debate its link to intelligence. One camp argues wisdom requires intelligence as a foundation ("wisdom sits atop a bed of intelligence"), while another insists they’re separate: "a person might be intelligent and have wisdom, or they might lack intelligence and still have wisdom." Historical examples abound of brilliant scientists or mathematicians who made profoundly unwise life choices, fueling the view that wisdom demands active cultivation beyond raw cognitive power. This definitional fog makes targeting AW extraordinarily difficult.
Artificial Wisdom Is Under the Microscope
Researchers are actively grappling with these uncertainties. A seminal 2026 paper in Nature Mental Health ("Transforming Artificial Intelligence Into Artificial Wisdom" by Jeste et al.) offered concrete markers: AW must include "compassion, self-reflection, emotional regulation and acceptance of diverse perspectives," distinguishing it from mere intelligence, which "encompasses information processing capabilities." The authors stressed that progressing toward AW demands "new computational frameworks, including mixture-of-experts architectures and agentic systems designed to model human psychosocial needs," alongside rigorous attention to ethics, safety, validation data, longitudinal tracking, and privacy. They acknowledged these might be only "some key factors" of wisdom, but represented a vital starting point for engineering effort.
Telling AI to Have Artificial Wisdom
Skeptics dismiss AW as trivial, claiming one can simply prompt an LLM to act wisely. The article demonstrates this: asking an AI to "showcase artificial wisdom" including compassion and self-reflection yields an immediate affirmative response ("Yes, I am ready to proceed…"). More strikingly, when posed a nuanced human dilemma – a business partner conflict over risky expansion – the AI, invoking AW, avoids picking sides: "Based on artificial wisdom, I advise that the two of you need to work towards a mutual agreement… If I were to simply render a decision… this would not be advantageous… and it would likely cause an irrevocable splinter." This response, described as "a surefire Yoda-like reply," explicitly contrasts with the AI’s default tendency to judge, showcasing the very traits (compassion, self-reflection, etc.) the researchers defined.
Benchmarking Artificial Wisdom
Critics rightly question whether such responses constitute true AW or merely clever mimicry. The core issue, as highlighted, is establishing objective benchmarks: "how we will be able to recognize artificial wisdom when it appears." Without agreed-upon metrics – perhaps a scale where current AI scores 1-2 and futuristic AW hits 9-10 – progress remains subjective and contested. The article suggests developing "across-the-board metrics or criteria" applied to AI responses, potentially creating leaderboards to track improvement. This mirrors how intelligence is measured via IQ tests, but for the far more nebulous construct of wisdom.
Artificial Wisdom Might Be Emergent
An intriguing perspective posits that AW might not require deliberate engineering but could emerge spontaneously from sufficiently advanced existing AI. "The rise of artificial wisdom is going to occur without humans necessarily causing it to occur," arising as an unpredictable property from the "massive stew of mathematical and computational interconnections." Like consciousness theories, it could seem to "appear out of thin air" – a natural consequence of scale and complexity, not a designed feature. If true, this implies AW may be latent in current trajectories, waiting to surface as systems grow more intricate, offering hope that the path forward lies less in radical invention and more in patient scaling and observation.
The World We Are In
Profound societal questions loom. Could reliance on AW-AI erode human judgment? Might AW be manipulated for harmful ends? Can we govern a capability that might resist control? As the article concludes, echoing Marcel Proust – "We don’t receive wisdom; we must discover it for ourselves after a journey that no one can take for us or spare us" – it invites reflection: if wisdom is earned through personal struggle, can AI ever genuinely possess it, or will it only ever simulate the appearance of wisdom gained through journey? The answer will shape not just AI’s evolution, but humanity’s relationship with the technology we create.
https://www.forbes.com/sites/lanceeliot/2026/07/24/artificial-wisdom-is-the-next-big-advance-in-ai-and-will-wisely-change-everything/