Knowledge is Power requires AI literacy

by Ian Farmer | May 22, 2025 | The Human Side of AI | 0 comments

Knowledge is power (from the latin “Scientia Potentia Est“) was the motto at my Melbourne primary school. From the days of Bacon—who famously declared “knowledge itself is power” (Bacon, as cited in Wikipedia contributors, 2025a)—and Hobbes—who wrote “the end of knowledge is power” in Leviathan (Hobbes, as cited in Wikipedia contributors, 2025a)—to the modern era, this phrase has underpinned the architecture of human progress.

Whether echoing through ancient proverbs or shaping Western philosophy, the control and distribution of knowledge have always determined who holds influence in society. Today, as Artificial Intelligence (AI) reshapes our world, we find ourselves once more at a crossroads.

Who will wield knowledge—and therefore power—in the age of AI?

 

Knowledge, Technology, and Shifting Power

Every major advance in information technology has reshaped society’s power structures. Perhaps the most pivotal example is the invention of the printing press by Johannes Gutenberg around 1440. Before Gutenberg, the creation of books was a laborious, manual process, making them expensive commodities accessible mainly to the ecclesiastical and aristocratic elite. The printing press, however, enabled the mass production of written materials, drastically reducing their cost and opening access to a far broader segment of the population (Fiveable, n.d.; PrintSafari, n.d.; Journalism.university, n.d.).

This leap in accessibility sparked a surge in literacy rates and allowed ideas to circulate as never before. It catalysed transformative movements such as the Renaissance, the Protestant Reformation—Martin Luther’s theses spread rapidly by print—and the Scientific Revolution (Journalism.university, n.d.). The press also made it possible to print in vernacular languages rather than just Latin, fostering the growth of national literatures and identities and challenging entrenched linguistic and cultural hierarchies (PrintSafari, n.d.).

Yet the printing press did more than democratise access to knowledge; it redefined who could create and interpret new knowledge. As literacy spread, the loci of cultural and intellectual authority shifted, challenging the status quo and fuelling the rise of new voices and movements (Fiveable, n.d.; Journalism.university, n.d.). The ability to engage with, shape, and question knowledge moved from a privileged few to a growing and more diverse public.

This historical precedent is essential for understanding the impact of AI today. Generative AI is often described as a revolution on the scale of the printing press—if not even greater (Singularity Hub, 2025). Where the printing press broadened access to books and ideas, AI is reshaping who can produce, manipulate, and judge the value of knowledge in the digital era. With every leap forward, the question evolves: who gets to participate, who benefits, and who is left behind (Wikipedia contributors, 2025b; Dewey & Bentley, 1949)?

AI and the Promise of Knowledge Democratisation

Supporters of AI see it as a leveller, breaking down old barriers to learning, creation, and opportunity (10xDS, n.d.; Sakai, n.d.; Xtra Mile, n.d.). AI models like ChatGPT can distil huge swathes of human knowledge and personalise it for individual users (Afroogh et al., 2024; Blogging Team, 2025). In education, AI’s potential is especially striking: virtual tutors can offer instant, adaptive feedback, supporting both students and teachers in ways previously unimaginable (UNESCO, 2021; UNESCO, 2024a; HMHCo, n.d.).

The reach of AI extends far beyond classrooms. Writing, design, data analysis, and more can now be accomplished by almost anyone with the right AI tools (Hultenius, 2024; Xtra Mile, n.d.). What once took years of training is suddenly accessible. In theory, this means talent and curiosity—not background or privilege—determine who can produce high-quality work (Reddit r/Lightbulb, 2023; World Economic Forum, 2025). This is not just about information: AI tools scaffold skill development, lower the cost of entry, and foster new waves of innovation and cross-cultural exchange (Afroogh et al., 2024; Gent, 2024; Sakai, n.d.; SME Today, n.d.).

Open-source AI movements seek to expand these benefits even further, advocating for free, widely available resources that enable global participation and collaboration (Sakai, n.d.; SME Today, n.d.). In short, AI is not merely making knowledge available; it is transforming who can do and create—potentially turning “everyone into an expert” (Hultenius, 2024).

The Double-Edged Sword: Illusions and New Inequalities

Yet, as with every leap forward, there is a darker side. The history of the printing press reminds us that new technology also creates new gatekeepers. Today, many of the most advanced AI tools are developed and controlled by a handful of powerful corporations, raising questions about bias, access, and hidden agendas (Afroogh et al., 2024; Technology & Society, 2025; UNESCO, 2025).

The apparent democratisation of expertise also comes with risks. AI can make anyone appear expert, but without deep understanding, critical thinking, or an ability to check for error, this can result in a dangerous “illusion of expertise” (Afroogh et al., 2024; Mata v. Avianca, 2023; Wikipedia contributors, 2025b). Outputs may sound authoritative while being flawed or even fabricated. The responsibility to verify and interpret now falls more heavily on the user, raising the bar for critical digital literacy (Afroogh et al., 2024; Blogging Team, 2025).

The AI Proficiency Chasm: New Power Hierarchies

Is AI really closing the gap, or is it carving out new divides? Emerging evidence points to an “AI proficiency chasm” that separates those who can effectively use AI from those left behind (World Economic Forum, 2025; Artha Learning, 2025; National CIO Review, n.d.; Business at OECD (BIAC), n.d.).

  • Economic and Geopolitical Divides: AI development is dominated by a few countries—primarily the US and China—who set the global agenda through patents, investment, and talent (Afroogh et al., 2024; World Economic Forum, 2025; OpenAI, 2023). The cost to participate at the frontier of AI research is now measured in tens or hundreds of millions, locking out most nations and organisations (Technology & Society, 2025).
  • Gender Disparity: The field of AI has a stark gender gap. Only 22% of AI professionals are women; senior roles are even more male-dominated (Randstad, 2024; Harvard Business School, n.d.; Interface, n.d.). Women are less likely to be offered AI training, less likely to use AI tools at work, and more likely to work in roles threatened by automation (Randstad, 2024; Harvard Business School, n.d.; World Economic Forum, 2025).
  • Age and Access: Older workers are also less likely to access AI training or use AI in their roles, further widening generational divides (Randstad, 2024; National CIO Review, n.d.; JMIR Aging, 2025; Kim, 2020; Brookings Institution, 2025). Without targeted upskilling, older adults risk being marginalised as AI becomes more prevalent (OECD, n.d.; World Economic Forum, 2025; Skillsoft, n.d.).

The result? Even as AI creates new opportunities, it can deepen existing inequalities. Job displacement is a real risk for those in routine or clerical work, while new AI-related roles often go to those with existing digital advantages (World Economic Forum, 2025; Kenan Institute, n.d.). The power to set standards, shape research, and attract top talent accrues to those who can afford it, compounding initial advantages and making it harder for others to catch up (OpenAI, 2023; Afroogh et al., 2024).

Table 1: The Dual Edges of AI—Democratisation vs. Polarisation

Potential for DemocratisationPotential for Polarisation
Access to InformationAI tools can synthesise and personalise vast amounts of knowledge, making it broadly accessible.Information can be controlled by a few entities; AI outputs may reflect biases in training data, limiting diverse perspectives.
Skill AcquisitionAI can lower barriers to learning complex skills and content creation, empowering more individuals.A significant AI skills gap persists; unequal access to quality training creates “AI haves” and “AI have-nots”.
Economic OpportunityAI can foster innovation and create new job roles, potentially opening avenues for diverse talent.AI may lead to job displacement for those in routine tasks and wage stagnation for those lacking AI proficiency; benefits accrue to the highly skilled.
InnovationAI can accelerate research and development, enabling rapid ideation and cross-functional collaboration.Concentration of R&D in a few entities (“AI oligarchy”) can narrow the research agenda and stifle diverse innovation paths.
Geopolitical InfluenceAI could foster greater global understanding and collaborative problem-solving.Dominance in AI capabilities can lead to an “AI oligarchy,” reinforcing existing geopolitical power imbalances and risking technological colonialism.

 

Closing the Gap: AI Literacy and Ethics

Bridging the AI divide requires more than technical fixes—it demands cultural change. AI literacy must become as fundamental as reading or arithmetic, equipping everyone to use, question, and help govern AI systems (boyd, 2024; UNESCO, 2021; Openeducationculture.org, n.d.).

AI literacy goes beyond coding. It means understanding what AI can and cannot do, how it is trained, and what biases and power dynamics it may embed (boyd, 2024; Boyd, D., 2024). It asks: who built this system? Who benefits? Who is at risk? (boyd, 2024; UNESCO, 2024a). UNESCO and other institutions are integrating AI literacy into educational frameworks, targeting not just students but the entire public (UNESCO, 2025; UNESCO, 2024b; HMHCo, n.d.).

However, skills alone are not enough. The ethical development and deployment of AI is critical to ensuring it serves society fairly, not just efficiently (Afroogh et al., 2024; Yates & APA, 2024; Brookings Institution, 2025). Ethical AI means designing and using these technologies to genuinely benefit intended communities, and should involve close collaboration with those who will be impacted or have relevant lived experience (Afroogh et al., 2024; Brookings Institution, 2025). This can include, for example, ensuring adequate and diverse representation in healthcare datasets to prevent biased outcomes, as well as robust data privacy protections.

Core tenets of ethical AI include:

  • Human-centred design: Keeping human values at the core of AI applications.
  • Transparency and Explainability: Systems should be open about how decisions are made (XAI).
  • Accountability: Mechanisms for redress when harms occur.
  • Public participation: Involving the wider community in AI governance (The AAMC, n.d.).
  • Equitable access: Addressing financial and social barriers to using AI.

Without such frameworks, the risk is not just technical bias, but the amplification of social inequities—and the phenomenon of “ethics washing,” where principles are espoused but not meaningfully acted upon (Afroogh et al., 2024; Yates & APA, 2024).

The Power of Neurodiversity in AI

True inclusivity in AI design means embracing cognitive diversity—recognising the unique strengths of neurodivergent individuals in pattern recognition, problem-solving, and creativity (Armstrong, 2010; Baron-Cohen, 2015; ResearchGate, 2024; Kelly, n.d.; SME Today, n.d.).

  • Neurodivergent contributors can spot subtle errors and biases, helping to create more robust and fair AI systems (Baron-Cohen, 2015; Armstrong, 2010).
  • Major employers such as Microsoft and JP Morgan now actively recruit neurodiverse talent, finding them exceptionally effective in technical roles (SME Today, n.d.; The Future of Work, n.d.).
  • Ethical AI must include not just “diversity” in the narrow sense, but the broadest possible spectrum of human thinking styles (Baron-Cohen, 2019; Scholarly Publishing Community, n.d.).

AI literacy and neurodiversity are mutually reinforcing: the more diverse the team, the better the questions they ask, and the more accountable, inclusive, and innovative the resulting technology (boyd, 2024; UNESCO, 2025; Armstrong, 2010).

Towards Collective Empowerment 

The challenge before us is clear. AI’s transformative potential will only be realised if we pursue collective empowerment, not just broader access to tools (Singularity Hub, 2025; Afroogh et al., 2024; Wikipedia contributors, 2025c).

  • The DIKW pyramid (Data, Information, Knowledge, Wisdom) is instructive here: while AI excels at processing data and generating knowledge-like outputs, true wisdom—judgement, ethics, social good—remains a distinctly human capacity (Wikipedia contributors, 2025c).
  • Knowledge, in the age of AI, is not static. It is transactional, iterative, and co-constructed in dialogue between humans and machines (Dewey & Bentley, 1949; Afroogh et al., 2024). The critical skill is not just using AI, but knowing how to question it, steer it, and understand its limits (Wikipedia contributors, 2025b).

If “knowledge is power,” then “shared knowledge is shared power.” Widespread AI literacy, strong ethics, and inclusive governance are all essential—not just for individual empowerment, but to sustain democratic accountability and social trust (boyd, 2024; UNESCO, 2025; Yates & APA, 2024).

The future is not determined by technology, but by the choices we make about how it is taught, governed, and used (Afroogh et al., 2024; UNESCO, 2025).

That future, at its best, is one where AI helps humanity not just to know more, but to be wiser, together.

 

 

References

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