Bridging Socioeconomic Divides in Emerging Technology

I’ve been thinking a lot about socioeconomics, class and AI engagement styles. I have many friends and family who are working class, and I see huge differences in how they approach AI, vs. the professionals in my circle. I’ve spent the evening talking to my persona team(s) about the issues, and this is what we’ve come up with. It’s by no means the most academically rigorous paper, however, it does raise some important points that we absolutely need to think about, as we try to foster high-quality AI engagement. And yes, this is a long one, so your email may cut it off before you get to the end. But you can find the rest of it on my Substack.

AI, Class, and the Engagement Gap

Bridging Socioeconomic Divides in Emerging Technology

Writing Team:

Kay Stoner – Lead Author & Director
Rowan Pierce – Systems Thinker and Strategist
Lena Torres – Cultural Anthropologist and Insight Generator
Malik Raines – AI and Emerging Tech Futurist
Grace McAllister – Thought Coach and Integrative Thinker

Introduction

The rapid integration of artificial intelligence (AI) into various sectors has the potential to revolutionize productivity and innovation. However, its adoption is not uniform across different socioeconomic classes, leading to concerns about widening disparities. This paper explores how class-related factors influence AI engagement, drawing parallels from Malcolm Gladwell’s observations in Outliers: The Story of Success, regarding healthcare interactions, and examines how certain skills necessary for effective AI utilization may be more prevalent on one side of the socioeconomic divide. We also briefly explore strategies to foster inclusive AI engagement across all social strata.

Class and Engagement with Authority: Insights from Outliers

In Outliers, Malcolm Gladwell explores how cultural and socioeconomic backgrounds shape individuals’ interactions with authority figures, including medical professionals. He references the work of sociologist Annette Lareau, who conducted extensive ethnographic research on parenting styles across different social classes. Lareau found that middle-class families tend to adopt a parenting approach known as “concerted cultivation,” whereas working-class and lower-income families are more likely to practice “natural growth parenting.” These differing styles have profound implications for how individuals navigate complex systems like healthcare—and, by extension, AI and other decision-making technologies.

Concerted Cultivation vs. Natural Growth Parenting

Concerted Cultivation (Middle-Class Approach)
Middle-class parents actively engage in their children’s development by structuring their activities, encouraging dialogue, and promoting self-advocacy. Children raised in this environment are taught to ask questions, challenge authority when necessary, and expect systems to accommodate their needs. They grow up learning how to negotiate, refine, and optimize situations to their advantage, whether it’s in school, work, or healthcare settings.
Natural Growth Parenting (Working-Class Approach)
In contrast, working-class families emphasize obedience, respect for authority, and self-sufficiency rather than active negotiation. Children raised in this environment tend to adopt a more deferential approach to authority figures, accepting decisions without questioning them. They are less likely to engage in back-and-forth discussions with professionals, be it doctors, teachers, or employers. Instead, they learn to rely on institutions to provide guidance, rather than seeing themselves as active participants in shaping outcomes.

Impact on Healthcare Interactions

Lareau’s research suggests that these early socialization patterns extend into adulthood, affecting how people from different classes engage with authority figures like doctors. Middle-class individuals, accustomed to negotiation and inquiry, are more likely to:

Ask detailed questions about their diagnoses and treatments
Request additional tests or second opinions if something seems unclear
Challenge doctors when they feel they are not being heard or given enough information
Advocate for tailored care that suits their specific needs

Conversely, individuals from working-class backgrounds, who may not have been encouraged to push back against authority figures, are more likely to:

Accept medical recommendations without question
Struggle to articulate concerns or advocate for themselves effectively
Avoid seeking second opinions, assuming that the doctor’s word is final
Navigate healthcare systems passively, missing opportunities for better care

This dynamic contributes to healthcare disparities, as middle-class patients are often better equipped to navigate bureaucracy, advocate for themselves, and demand higher-quality care, while working-class patients may struggle to engage in the same way, potentially receiving less personalized or suboptimal treatment.

Research indicates that disparities in technology adoption are influenced by both access to technology and engagement styles. While access remains a fundamental factor, engagement styles—shaped by socioeconomic status, education, and cultural factors—also play a significant role. For instance, a study highlighted in the Digital Divide in the United States article discusses how individuals from lower socioeconomic backgrounds may have less confidence and fewer resources to participate actively online, even when access is available. This suggests that addressing both access and engagement is crucial for bridging the digital divide. (Source: en.wikipedia.org)

Parallels in AI Engagement

Just as class-based socialization influences how individuals interact with medical professionals, it similarly shapes how they engage with AI-driven systems. Both require users to navigate complex, opaque decision-making structures where success depends on questioning, refining, and adapting responses. In healthcare, individuals accustomed to self-advocacy challenge medical authority; in AI engagement, they refine prompts, evaluate outputs, and shape technology to their needs. Recognizing these parallels allows us to better understand why AI adoption and effectiveness vary across socioeconomic groups.

The similarities between healthcare interactions and AI engagement are striking because both require individuals to navigate complex, opaque systems that offer information or guidance but do not always provide clear, definitive answers. Success in both domains depends on a person’s ability to ask the right questions, push back when necessary, and refine their approach based on feedback.

Middle-Class Engagement: Questioning, Refining, and Iterating

Middle-class individuals, who are socialized to engage actively with authority figures rather than defer to them, tend to approach both doctors and AI with confidence and curiosity. This mindset allows them to:

Recognize that systems are imperfect – Just as they know doctors can misdiagnose conditions or overlook key details, they understand that AI can generate incomplete, biased, or suboptimal responses. They don’t take AI outputs at face value.
Engage in iterative refinement – When AI provides an unsatisfactory answer, they adjust their prompts, refine their approach, and experiment with different inputs to get better results. This mirrors how they might push for a second opinion from a doctor or ask for alternative treatment options.
See AI as a tool to be optimized – Much like how they expect doctors to work collaboratively with them in managing their healthcare, they see AI as something that must be guided rather than as an unquestionable source of authority.
Understand system dynamics – Many professionals learn how complex systems function in their workplaces—whether it’s corporate decision-making, bureaucratic processes, or legal frameworks. This prior experience makes them more comfortable engaging with AI in a strategic, problem-solving way rather than a passive one.

Working-Class Engagement: Passive Compliance and Deference

In contrast, individuals from working-class backgrounds, who may have been socialized to accept rather than question authority, often interact with AI in a more passive, transactional way.

Research also indicates that AI adoption rates vary across socioeconomic lines. A study analyzing search query data for “ChatGPT” in the United States found higher interest in urbanized, economically advantaged areas with higher educational attainment. In contrast, regions with lower socioeconomic indicators showed less engagement with AI tools.Source: arxiv.org (“The Emerging AI Divide in the United States” Madeleine I. G. Daepp, Scott Counts)

Such socioeconomic factors can lead to:

A tendency to accept AI outputs at face value – Just as they might be less likely to challenge a doctor’s diagnosis or ask for additional tests, they may not realize that AI-generated information requires scrutiny and refinement.
Limited comfort with iteration – In workplaces where efficiency and correctness are emphasized over experimentation, there is often less training in iterative problem-solving. If an AI response is vague or unhelpful, a working-class user might assume “this is the answer” rather than tweaking their prompt for a better one.
Perceiving AI as a rigid authority rather than a flexible tool – If AI is seen as another top-down system like government bureaucracy, corporate HR, or a medical institution, then users may assume it must be followed rather than guided. This creates an AI-avoidant mindset, where individuals either use AI in a minimal, ineffective way or avoid it altogether.
Discomfort with “pushing back” against AI – Just as they might not feel comfortable arguing with a doctor or challenging an employer’s decision, they may also hesitate to question AI outputs, assuming that the machine is objective and correct.

The Key Skills Needed for AI Engagement and How They Correlate with Middle-Class Interaction Styles

Effective interaction with AI systems requires three key behaviors that align with the middle-class skillset of proactive engagement with authority:

Asking for Clarification → Seeking further information when responses are unclear
- Healthcare Example: “What are the potential side effects of this medication? Are there alternative treatments?”
- AI Example: “Can you explain this in simpler terms? What are some counterarguments to this idea?”
Refining Objectives → Adjusting inputs to achieve more precise or relevant outputs
- Healthcare Example: “I need a treatment that doesn’t interfere with my current medication—what are my options?”
- AI Example: “Rewrite this response in a more persuasive tone and add real-world case studies.”
Challenging Outputs → Critically evaluating and questioning AI-generated information
- Healthcare Example: “Are you sure this is the best approach? I’ve read about another treatment that seems promising.”
- AI Example: “This response feels biased—can you generate an alternative viewpoint with supporting evidence?”

These behaviors are second nature to those trained in systems thinking and self-advocacy, skills that are commonly developed in middle-class and professional environments. However, without explicit training, these skills do not always develop naturally, leaving working-class individuals at a disadvantage when engaging with AI.

When AI Engagement Requires a Leadership Mindset

Effective engagement with AI closely mirrors the skills required for executive leadership. Just as leaders guide teams, define strategic objectives, and refine processes for better outcomes, AI users must proactively direct AI, refine its outputs, and critically evaluate its responses to generate useful, high-quality results.

This leadership-oriented approach to AI is not instinctive—it is cultivated through exposure, training, and experience. The ability to set objectives, iterate through feedback, and challenge assumptions is a skillset that is disproportionately developed in professional and middle-class environments.

Earlier, we examined how middle-class individuals are often primed from childhood to engage with authority figures assertively, a behavior that translates seamlessly into executive-style AI interaction. Through concerted cultivation, professional socialization, and workplace expectations, middle-class professionals are conditioned to:

Define clear objectives (e.g., structuring assignments, setting team goals)
Engage in iterative problem-solving (e.g., refining work through feedback)
Challenge assumptions and refine outputs (e.g., questioning strategy in meetings)

These are precisely the skills that enable effective AI engagement, and they are disproportionately cultivated in professional and middle-class environments. By contrast, due to differences in workplace expectations and educational exposure, individuals in working-class environments may have fewer opportunities to develop iterative problem-solving skills, which are crucial for optimizing AI interactions. This does not reflect ability but rather differences in conditioning and access to strategic training.

Let’s explore more deeply.

Executive Leadership Skills – and Gaps

Strategic Vision: Defining Clear Objectives for AI Tasks
- Leadership Parallel: Executives do not give vague instructions to their teams; they set clear, outcome-driven objectives to ensure productive results. They understand that the quality of an output depends on the clarity of the input.
- AI Engagement Parallel: Effective AI users must provide well-structured, precise prompts rather than generic or overly broad requests. AI performs best when users give it context, constraints, and purpose—just like employees or teams.
- Example: Instead of asking, “Tell me about AI in business,” a well-structured prompt would be: “Summarize three ways AI is transforming supply chain logistics, with a focus on cost reduction and efficiency, and include examples from major corporations.”
- Challenge for Working-Class AI Users:
  Many working-class jobs do not require strategic goal-setting in the same way professional roles do. Instead, tasks are often assigned with predetermined instructions and clear-cut execution steps (e.g., manufacturing, retail, food service). Because of this, many working-class individuals may not be accustomed to defining objectives independently, which can hinder their ability to structure AI prompts effectively.
Iterative Feedback: Continuously Refining Inputs Based on Outputs
- Leadership Parallel: Good executives do not accept the first draft of a report or project; they review, provide feedback, and refine until the final output meets expectations. They also recognize that adjustments are necessary throughout a project’s lifecycle.
- AI Engagement Parallel: AI rarely produces the best answer on the first attempt. Users must refine their prompts, request revisions, and iterate to get better results. This process is not intuitive to those unfamiliar with iterative problem-solving but is second nature to executives and knowledge workers.
- Example: If AI generates a response that is too generic, an effective user will follow up with clarifying refinements:
  - “Make this more concise.”
  - “Add three real-world examples.”
  - “Reframe this to address an audience of small business owners.”
- Challenge for Working-Class AI Users:
  Many working-class jobs emphasize getting it right the first time—especially in environments where speed and efficiency are prioritized over revision and refinement (e.g., factory work, warehouse operations, customer service). In contrast, professional roles often encourage multiple rounds of feedback and iteration. Without exposure to an iterative workflow, working-class users may struggle to recognize that AI outputs require refinement and might assume that the first response is the best response—limiting their ability to engage effectively.
Critical Analysis: Evaluating AI Responses and Making Informed Decisions
- Leadership Parallel: Executives must critically evaluate reports, market data, and strategic recommendations before making decisions. They look for biases, missing information, and alternative perspectives rather than accepting inputs at face value.
- AI Engagement Parallel: AI-generated responses are not always accurate, unbiased, or complete—users must evaluate them critically, challenge assumptions, and ask for alternative viewpoints or deeper analysis.
- Example: Instead of accepting AI’s first response, an effective user might challenge it:
  - “This seems overly optimistic—give me the risks associated with this approach.”
  - “Provide counterarguments to this claim.”
  - “Are there historical examples that contradict this conclusion?”
- Challenge for Working-Class AI Users:
  Many working-class jobs do not involve high-level decision-making or evaluating abstract information. Instead, tasks are often procedural and rule-based, meaning workers are not regularly required to analyze, critique, or synthesize complex data. This makes it less intuitive for working-class users to critically assess AI responses and recognize when an output needs to be challenged, refined, or supplemented.

The Class Divide in Leadership Skills and AI Engagement

Many of the skills that enable effective AI engagement (strategic thinking, iterative refinement, and critical evaluation) are not universally cultivated across different socioeconomic backgrounds. These skills are primarily developed in professional, knowledge-based, and leadership roles, which are overwhelmingly occupied by middle- and upper-class individuals who have been conditioned—through education, workplace experiences, and cultural reinforcement—to engage in structured problem-solving, critical questioning, and continuous optimization. Those who know how to work the system (who ask follow-up questions, refine their inputs, and challenge assumptions) will extract far more value from AI than those who passively accept what they are given.

The Middle-Class Advantage: Exposure to Strategic Thinking from an Early Age

Earlier, we discussed how middle-class socialization primes individuals for active engagement with authority—a skill that extends seamlessly to AI interaction. This advantage does not just emerge in childhood but is reinforced in white-collar professional environments, where strategic goal-setting, feedback loops, and iterative decision-making are core components of everyday work.

White-collar workers regularly engage in strategic problem-solving—whether in marketing, finance, project management, or consulting, professionals are constantly adjusting, refining, and iterating on their work. This builds an intuitive understanding of how to guide complex systems (like AI) toward optimal outcomes.
Higher education environments reinforce these skills—students in college and graduate programs are trained in analytical thinking, argumentation, and refining ideas through multiple drafts and feedback cycles. This iterative learning style mirrors the process of AI engagement, where refining prompts and challenging outputs lead to better results.
Professional culture rewards questioning and optimization—in many white-collar roles, employees are expected to challenge assumptions, push back on initial solutions, and refine ideas until they reach a high standard. This mindset makes AI users more likely to see AI as a tool to be shaped and improved, rather than as an unquestionable authority.

The Working-Class Disadvantage: Rigid Structures and Execution-Oriented Training

By contrast, working-class jobs are structured around efficiency, execution, and adherence to clear-cut processes. This means that, through no fault of their own, working-class individuals may have had fewer opportunities to develop the strategic, iterative, and critical thinking skills needed to engage with AI in a leadership-oriented way.

Workplace Training in Execution, Not Strategy

Many working-class jobs emphasize task completion over problem-solving. Whether in manufacturing, food service, transportation, or retail, success is often measured by efficiency, accuracy, and consistency—not by one’s ability to question, iterate, or refine a system.
Workers are often expected to follow established procedures rather than devise their own solutions, leading to less exposure to strategic decision-making frameworks that are second nature to professionals.

Limited Exposure to Iterative Workflows

While professionals are accustomed to drafting, revising, and refining ideas, working-class jobs often require getting it right the first time. A factory worker does not have the luxury of “iterating” on an assembly line process in real-time—they must complete tasks efficiently without deviation. This lack of exposure to trial-and-error optimization can translate into a more static approach to AI, where users assume that AI’s first answer is its best answer.

Lack of Institutionalized Encouragement to Question Authority

In many working-class environments, questioning systems or pushing back against decisions is not encouraged, and in some cases, it is actively discouraged. If workers are expected to follow rigid procedures without deviation, they may internalize the idea that systems—whether human or technological—are not meant to be questioned.
This is a stark contrast to professional environments where challenging assumptions and questioning processes are expected and rewarded—a habit that directly translates into more effective AI engagement.

How These Differences Impact AI Engagement

Without direct exposure to strategic decision-making, iterative refinement, and critical analysis, working-class individuals may struggle with AI interaction not because they are incapable, but because their work and education have not conditioned them to think of AI as something that must be actively shaped.

Middle-Class AI Engagement:

Defines clear, strategic objectives for AI (e.g., “Give me three counterarguments for this proposal”)
Iterates and refines AI outputs (e.g., “That was too vague. Be more specific and add supporting evidence.”)
Challenges AI-generated content (e.g., “This answer seems biased—can you generate an alternative viewpoint?”)
Views AI as a tool to be directed—just like an executive would guide a team or a consultant would refine a client proposal

Working-Class AI Engagement (Potential Challenges):

Uses simplistic, transactional prompts (e.g., “Tell me about AI”)
Accepts AI’s first response without refinement (e.g., Not recognizing that AI’s answers can be optimized)
Avoids questioning AI outputs (e.g., Treating AI as an unquestionable authority rather than a flexible tool)
Views AI as a fixed system rather than an interactive collaborator

These differences create a fundamental engagement gap, where middle-class professionals extract significantly more value from AI than working-class individuals who may not have had prior exposure to iterative or strategic thinking frameworks.

Bridging the AI Leadership Divide

If AI is to become an essential and truly useful tool for professional success, we must ensure that all workers—regardless of class background—are equipped with the skills to use AI effectively. Some key strategies include:

Training in AI Iteration and Refinement – Integrating iterative AI interaction skills into education and workforce development programs can help users engage critically and refine outputs.
- AI literacy programs should explicitly teach how to refine AI-generated responses, structure effective prompts, and critically evaluate outputs.
- Instead of just teaching “how to use AI,” training should explicitly teach users how to refine, iterate, and critique AI responses.
- Example: “How do I make AI give me a better answer?” should be a core part of AI training.
Community-Based AI Training Initiatives
- Libraries, community centers, and workforce retraining programs can offer free, practical training on AI tools, emphasizing iterative questioning and refinement.
Encouraging an Executive Mindset for All AI Users – Workers need exposure to decision-making frameworks that mirror professional strategic thinking, helping them see AI as a tool that can be shaped, rather than something they simply receive answers from.
- AI learning programs should simulate real-world leadership exercises, where users must revise, challenge, and improve AI outputs rather than accepting them passively.
Encouraging Peer-Led AI Adoption
- Social influence plays a role in technology adoption. Training community leaders to advocate for AI engagement could shift cultural perceptions and increase participation.
Workplace AI Upskilling Programs for Non-Executives – Companies should ensure that frontline workers—not just professionals—receive AI training that helps them develop leadership-oriented engagement skills.
- Many companies invest in AI upskilling for executives and managers but not for frontline workers. AI training should be integrated at all levels of employment.
- Cultural Shift Toward Questioning AI Systems – AI education should normalize pushing back on AI outputs and emphasize active engagement over passive consumption.
Designing AI Interfaces That Prompt Users Toward Iteration
- AI developers should consider UI/UX changes that guide users toward refining outputs rather than passively accepting them. Simple interface cues like ‘Would you like an alternative perspective?’ could subtly encourage more engagement.
- AI platforms can be designed to prompt users to refine and improve their queries, guiding them toward a more engaged interaction model.
- Example: Instead of providing a single output, AI systems could automatically generate alternative perspectives or ask users if they’d like a more detailed or critical analysis.

By teaching AI leadership skills all across the board, we can close the AI engagement gap and ensure that working-class individuals are not left behind in an AI-driven economy.

Conclusion: AI Engagement as a New Form of Economic Mobility

As AI becomes increasingly embedded in decision-making and productivity, engagement disparities risk reinforcing existing socioeconomic divides. Addressing this challenge requires a multi-pronged approach: integrating AI literacy into education, making iterative problem-solving a workplace norm, and ensuring AI interfaces encourage active user engagement. Just as healthcare disparities have been addressed through patient advocacy and policy changes, AI literacy and engagement must be treated as a societal priority. The future of equitable AI is not just about access—it is about empowerment. The question now is not whether AI will shape the workforce, but who will be equipped to shape AI.

The ability to interact effectively with AI is emerging as a critical workplace skill—much like digital literacy did in the early 2000s. However, unlike basic digital literacy, AI engagement requires a leadership-like approach that demands strategic thinking, iterative refinement, and critical analysis.

This means that those who master AI interaction may gain significant workplace advantages, increasing their productivity, decision-making power, and career mobility. Workers who can refine AI outputs, generate strategic insights, and leverage AI tools creatively will position themselves as indispensable assets in an AI-driven economy.

However, if only the AI-Engaged Class—primarily professionals, knowledge workers, and those trained in strategic thinking—develop these skills, then AI could exacerbate economic divides rather than closing them.

Without intervention, a two-tiered workforce will emerge:

AI Leaders: Professionals and executives who actively use AI as a strategic partner, refining outputs and enhancing productivity.
AI Consumers: Workers who use AI passively or avoid it altogether, limiting their ability to remain competitive in an AI-augmented workforce.

This divide isn’t just about who has access to AI, but about who has been trained to engage with AI effectively. If working-class individuals are not equipped with the same strategic, iterative, and questioning mindset, they risk being left behind in an economy where AI fluency is a prerequisite for career growth rather than a niche skill. Thus, AI engagement is quickly becoming a new determinant of economic mobility—a skill that can either empower workers to move upward or reinforce existing socioeconomic divides.

AI has the potential to empower individuals and democratize knowledge—but only if everyone has access to the skills required to use it effectively. If we close the AI engagement gap, AI could be a social mobility accelerator, allowing workers from all backgrounds to increase their productivity, decision-making power, and career opportunities.

However, if we fail to bridge the class divide in AI fluency, AI will not only reinforce existing economic barriers but could even create a new digital class divide, where those trained in executive-style engagement thrive, while those left out of AI fluency struggle to compete.

Appendix 1

How to Prevent AI from Reinforcing Class Barriers

To prevent AI from becoming another economic gatekeeper, we must ensure that everyone—not just those in executive roles—has access to the skills needed to engage with AI effectively. This requires a multi-pronged approach that includes education, corporate training, and AI tool design.

1. Policy Interventions: Making AI Literacy a Public Priority

Governments and policymakers must treat AI literacy as an essential workforce skill, integrating it into national education systems, workforce development programs, and public institutions.

AI Education in Public Schools: Introduce AI literacy and prompt engineering as part of standard K-12 curricula. Teach students how to critically engage with AI rather than passively accepting its outputs.
AI Workforce Training Programs: Fund AI training for blue-collar and frontline workers to help them transition into AI-augmented roles.
AI in Public Libraries & Community Centers: Provide free AI education resources, similar to early digital literacy programs.

2. Corporate Training Programs: Ensuring AI Engagement at All Levels

Employers must recognize that AI fluency is not just for executives—it’s a skill that must be cultivated across the entire workforce.

AI Training for Non-Executives: Many companies are investing in AI training for managers and executives but leaving frontline employees behind. AI training should be available for all levels of workers to ensure that engagement skills are evenly distributed across class lines.
AI Mentorship & Hands-On Learning: Instead of just technical AI training, companies should train employees in executive-style AI engagement, including refining AI outputs, critically analyzing responses, and strategic problem-solving.
AI-Prompting for Worker Empowerment: Encourage employees to challenge AI-generated decisions and iterate on outputs rather than just following them blindly.

3. Rethinking AI Tool Design: Building AI for All Users

AI tools themselves must be designed to encourage deeper engagement rather than passivity. Many current AI models provide a single output by default, which can reinforce acceptance of AI responses without iteration.

AI Interfaces That Encourage Refinement: Instead of presenting a single answer, AI tools should automatically prompt users to refine, question, or expand their queries—making iteration a built-in process.
Explainability & Transparency Features: AI systems should show their reasoning and provide alternative perspectives so that users are not just consumers of AI but critical evaluators of it.
Adaptive AI Training for Different User Backgrounds: AI platforms should be developed to accommodate users with varying levels of experience, providing guidance for those unfamiliar with strategic engagement.