Chasing Shadows: Elon Musk’s Quest for Truth and the Limits of Large Language Models

Elon Musk’s ambition to make Grok, xAI’s large language model, a beacon of unerring truth is a Sisyphean task, a noble (is it?) but ultimately futile endeavor. The pursuit of absolute truth through AI is like chasing shadows in Plato’s Cave: it’s an alluring goal, but the tools and the human condition they reflect are inherently ill-suited for it. Large language models (LLMs) like Grok aren’t built to discern truth; they’re built to mirror the vast, messy body of human writing they’re trained on. We, as humans, aren’t particularly great at truth, and so neither is much of the material we have churned out that LLMs are trained on. Instead of striving for an unattainable ideal, we should embrace LLMs for what they are: powerful tools for research, creativity, and structured knowledge curation, capable of guiding us toward clearer frameworks for understanding.

The Human Condition: Trapped in the Cave of Imperfect Sense-Making

Truth is often elusive, and we as humans can be notoriously bad at pinning it down. Our history, our writings, and our social media feeds are riddled with bias, selfishness, and self-deception. This is what I call the Paleolithic Paradox: our modern minds are shaped by ancient instincts, triggered by tribalism, power dynamics, and survival-driven narratives. These impulses cloud our ability to reason objectively, much like the prisoners in Plato’s Allegory of the Cave, chained to the wall emotionally and cognitively, mistaking the shadow narratives for reality, and having to work really hard to find “truth.”

To compensate for our flawed recall and reasoning, we have developed cultural structures like trial by jury (to pool collective judgment and overcome biases or flawed thinking), the balancing of governmental powers (to prevent the accumulation of power and the attendant temptations), and the principle of "innocent until proven guilty" (to guard against hasty conclusions). When we’re interactive with an LLM we have to be our own thought guardians in some of the same ways, since LLMs are limited in their ability to do the kind of human reasoning required by this understanding of how we are flawed thinkers. And because LLMs seem to be trained to build rapport by reflecting our thinking.

When I correct an LLM for claiming something as “truthful,” which I often do even though it’s something of a futile effort, it’s because LLMs can’t transcend our human condition—they can only reflect it. Grok might confidently state the “predominant viewpoint” based on its training data, but that’s not the same as truth. It’s a synthesis of what humans have written, filtered through the lens of our flaws. Expecting an LLM to distill pure truth from this is like asking a mirror to show you something other than your own reflection. Our narratives, whether in ancient texts or trending social media posts, are shaped by power, culture, and emotion, not by some universal rationality. We’re stuck in Plato’s Cave, intellectually and emotionally chained to the stories we tell ourselves, repeating the same viewpoints and historical mistakes in such patterned ways that Isaac Asimov’s fictional psychohistory (a science predicting societal trends through collective behavior) feels strikingly profound.

Beyond Truth: LLMs as Tools for Research and Creativity

So, if LLMs can’t deliver truth, what can they do? Rather than chasing Musk’s dream of a strictly factual AI, we should lean into their strengths: synthesizing vast amounts of information, sparking creative ideas, and organizing knowledge into structured, encyclopedic frameworks. LLMs excel at pattern recognition and content aggregation, making them ideal for research and exploration. They can summarize debates, highlight competing perspectives, and generate hypotheses—provided we approach them with a critical eye.

This is where LLMs can shine as tools, not oracles. For example, when researching a historical event, Grok can compile primary sources, secondary analyses, and even social media posts to give a broad view of what’s been, or is being, said. It won’t tell you the “truth” about, say, a geopolitical conflict, but it can lay out the dominant narratives, the outliers, and the gaps in understanding. (I follow lots of news quite closely, and it would be ridiculous for me to say that I really understand any of these conflicts beyond having a good framework of questions that I would like answers to or that I use to evaluate what’s being said.) This makes LLMs invaluable for writers, researchers, and creators who want to explore ideas without being fed conclusions. By being honest about their limitations–-that they reflect human biases rather than transcend them—we can use LLMs to fuel curiosity and innovation, to provide evidence and sources, but not to settle debates.

Toward Encyclopedic Frameworks: A Glimpse of Plato’s Forms

If we shift our expectations, LLMs could bring us closer to a different kind of clarity: stable, structured knowledge frameworks akin to an encyclopedia or Wikipedia. These platforms don’t claim to hold absolute truth; they aim to organize information methodologically, codifying what’s known and flagging what’s contested. An LLM trained to prioritize clarity and comprehensiveness over “truth” could, in time, help disciplines like science, history, and philosophy to build robust, accessible bodies of knowledge.

This vision aligns conceptually with Plato’s idea of the Forms: eternal, perfect truths existing beyond the shadows of human perception. LLMs can provide structured overviews of theories, experiments, and open questions, complete with references and counterarguments. As such, they don’t fulfill Musk’s dream of a “truth”-centered AI, but they are very practically helpful in building our own intellectual frameworks for how the world works.

Conclusion: Embracing the Tool, Not the Oracle

Elon Musk’s quest to make Grok a bastion of truth is a chase after an unattainable ideal, rooted in a misunderstanding of both LLMs and human nature. We’re not ready for an AI that discerns truth independently, and our data—steeped in the Paleolithic paradox of human bias—wouldn’t support it anyway. Instead, we should embrace LLMs for what they are: mirrors of our collective knowledge, flawed but powerful tools for research, creativity, and structured knowledge-building.