RAGs to riches

Historically, societies justified inequality through religion, such as kings' divine right or nobility's sanctity. Enlightenment ideals like "all men are created equal" challenged these narratives, inspiring revolutions and constitutional reforms. However, inequality persists, and modern society has replaced religious justifications with meritocracy—the belief that success is earned through effort and talent.

In the book The Myth of Meritocracy, According to Michael Sandel, published in 2021, Philosopher Michael Sandel critiques the concept of meritocracy, arguing that it often perpetuates inequality rather than alleviating it. He suggests that the meritocratic ideal can lead to a sense of entitlement among the successful and resentment among those left behind.

Meritocracy is upheld by the promise of equality of opportunity, but evidence paints a more complicated picture. Social mobility—the cornerstone of meritocracy—is limited in many countries. Research highlights phenomena like "sticky floors" and "sticky ceilings," where the poorest and richest remain entrenched across generations. Furthermore, the persistence of elite status, such as through "Nepo Baby" families in media and business, challenges the notion that hard work alone dictates success.

Paradoxically, countries with greater inequality often strongly believe in meritocracy, a phenomenon sociologist Jonathan Mijs calls the Paradox of Inequality. The more unequal a society is, the more its inhabitants cling to the illusion that the system is fair, even as social mobility declines.

AI as a Catalyst for Equity

The persistence of inequality challenges the meritocratic ideals, but LLMs present a unique opportunity to disrupt entrenched systems. By democratizing access to knowledge and reducing the barriers to learning and innovation, these tools could empower individuals and communities to overcome structural limitations.

LLMs democratize knowledge by breaking traditional barriers and providing high-quality, accessible information. They enable open access to expertise in fields such as medicine, programming, and law, making it available to anyone with internet access. Through real-time translation, LLMs support language inclusion, allowing users to learn in their native languages and reach underserved populations. Additionally, they offer customizable learning experiences by delivering tailored, on-demand answers, simplifying complex topics for users at any level of expertise.

Flattening Learning Curves

LLMs significantly reduce the effort required to gain expertise by streamlining the learning process. Synthesizing vast amounts of information makes it easier to understand advanced concepts quickly, allowing users to bridge knowledge gaps efficiently.

Additionally, LLMs provide skill training in diverse areas, from coding tutorials to resume writing, empowering individuals to learn and apply new skills independently. Their ability to deliver real-time feedback enhances learning further, as conversational AI offers instant corrections and explanations, fostering a deeper understanding of complex topics.

Breaking Structural Barriers

By making LLMs widely available, they reduce dependency on costly formal education systems, opening opportunities for individuals regardless of socioeconomic status.

LLMs also promote innovation by providing small businesses and entrepreneurs with AI-driven insights, leveling the playing field against larger competitors. Moreover, they help dismantle institutional gatekeeping by offering knowledge that once required access to elite universities or organizations, enabling broader participation in previously exclusive domains.

Challenges and Limitations

Despite their promise, LLMs face significant challenges that could limit their impact on structural inequality. Access disparities remain a key obstacle, as only some have reliable internet, devices, or digital literacy to benefit from these tools.

Bias in AI systems, stemming from the data they are trained on, may unintentionally reinforce existing inequalities. Economic monopolization also poses a risk, as proprietary AI platforms might restrict access to those who cannot afford them. Furthermore, over-reliance on AI for knowledge acquisition could undermine critical thinking and limit the development of deeper expertise among users.

To truly rebalance structural determinism, LLMs must be integrated into broader societal reforms. Promoting open-source AI models can ensure inclusivity and prevent monopolization, allowing wider access to these transformative technologies.

Public policy support, such as subsidies for AI tools and investments in digital infrastructure, can further democratize their benefits. Lastly, empowering communities with training and resources to effectively use LLMs can maximize their impact, helping to bridge divides and create a more equitable society.

A Step Forward

While LLMs are not a panacea, they represent a significant step toward flattening hierarchies and rebalancing the scales of opportunity. The promise of LLMs lies not just in their ability to teach us new skills but in their capacity to inspire a more inclusive and just society—one where knowledge empowers individuals to create new opportunities free from the shackles of incumbent cultural modalities.