By Steven Abernathy

Background

This paper began as a summary of a 3-day private equity investment research project for The Abernathy Group Family Office. As research progressed during the trip, we realized the changes available and likely to be implemented were far larger and potentially more disruptive than we believed when I boarded the plane.

The primary goal was to find out whether America’s educational infrastructure would embrace the changes being introduced by leveraging newly available technologies. The secondary goal was to understand how and when the technologies would best be introduced and implemented, and whether our ineffective, staid educational system would remain unchanged.

The founders/owners of the private university welcomed the visit, and as the conversation began, it became clear I was out of my “circle of competence.” It became obvious that shifts in education, healthcare, and many other educational verticals will reshape our economy in ways not seen since the Industrial Revolution.

I needed to understand the major shifts, dislocations, and demands education must meet to train our young adults and maintain our country’s place in the world order. Here’s why.

Education has been, and remains, one of the most relevant indicators of economic prosperity. Education is key to maintaining our globally advantaged position. Our U.S. educational system must incorporate the financial and technological changes driven by the burgeoning technologies becoming available over the next few years, or risk losing our status as a global hegemon.

Because of this realization, the goal of this paper has pivoted to finding answers to the questions educational industry experts are, and will be, asking about the future before us.

Note: There is a high probability that I have overlooked many important questions that are key to unlocking the future of education. However, here is my first humble cut of the questions that need answers so far.

Partial List of Questions Needing Answers Before Making Decisions

• What will the job market demand of employees and management over the next 10+ years?
• Which of those jobs will require basic degrees, advanced degrees, or certificates, as opposed to demonstrated competencies?
• How will the delivery of education change with the soon-to-come implementation of virtual reality (VR), augmented reality (AR), and 6G connectivity, combined with edge computing?
• What will the teacher’s role become when each student has Ph.D.-level data at their fingertips that never tires and always provides (largely) reliable results?
• How will changes in the hiring process influence education’s delivery? (This is the second-order benefit/effect of many educational institutions, as they may be rated relative to their job placement statistics.)

• How quickly will Artificial Intelligence (AI) change the implementation and delivery of education?
  • Change too quickly, and our economy enters a recession as job displacement will outweigh Artificial Intelligence’s benefits. (*This also risks creating long-term rejection of this transformational technology, potentially souring the citizenry’s opinion of Artificial Intelligence.)
  • Change too slowly, and the U.S. economy will decline into a recession as corporate earnings lag the enormous spending required to create Artificial Intelligence; also, rival countries (China, for instance) are already reaping the benefits of a more educated society (6 days a week, no summer holiday for young students).

• What impact will educational change have on the existing institutional infrastructure already in place in the U.S. and globally?

1. The Pedagogical Imperative: Solving the Scalability Crisis

The global educational landscape is undergoing a wholesale transformation. Education and learning models are shifting from the current industrial “mass-produced” model to a personalized, scalable “Tutor Model” powered by artificial intelligence (AI). Organizations will soon be fundamentally rewiring their pedagogical contracts to capture education’s long-term strategic value and embrace the technological advantages of a) virtual reality (VR), b) augmented reality (AR), c) 6G, and d) A.I., thereby transitioning from a legacy of standardized instruction (1 teacher to 30/40 students) to a high-touch educational ecosystem (Mastery-Based Learning) that delivers near one-to-one delivery of education (the Tutor-Student model) at scale.

Research clearly demonstrates that this evolution is a strategic necessity for global economic resilience. Traditional 1-teacher-to-30-student systems were never designed for the precision required in a post-industrial economy; the 1-to-30 model frequently traps students in an “intervention purgatory” where progress is hindered by fragmented data, delayed feedback, and social and peer pressures, all compounded by limited teacher bandwidth. The strategic foundation of this evidence-based shift in delivery methodology lies in resolving Benjamin Bloom’s “2-Sigma Problemⁱ.” Bloom’s 1984 research proved that students receiving one-on-one tutoring combined with mastery-based learning (MBL) performed 2 standard deviations (2SD) better than those in traditional classrooms. Students reached the 98th percentile faster and with fewer hours of concentrated instruction.

The problem, both societally and economically, was that tutor-based education was so expensive that it was accessible only to the elite, which tended to create, widen, and sustain a class system. Although scaling human-to-human instruction has traditionally been costly, integrating Generative AI (GAI) and Analytical AI offers the first practical approach to democratize and scale this “Tutor Model” across the global Total Addressable Market (TAM) at equal or lower cost.

Education Model	Success Rate (Percentile)	Availability & Cost Structure
Traditional Classroom	50th percentile	Largely available everywhere today.
Mastery-Based Learning (MBL)	84th percentile	Not widely available; requires differentiated group instruction, which prohibitively increases costs.
AI-Powered Tutor (GAI; AI + Human Hybrid Model)	90th percentile	Not widely available today; experimentally gaining traction at costs similar to the traditional classroom.

Traditional Classroom – delivers 50^th-percentile success; widely available everywhere today.

Mastery-Based Learning (MBL) delivers 84th-percentile success; it is not widely available because it requires differentiated group instruction, which prohibitively increases costs.

A.I. + Human Hybrid Model – 2.0 sigma type Instruction – 93^rd to 98^th percentile success; not widely available today; (includes A.I. Scaffolding + Human Mentorship)

The mechanics of this shift are defined by the “Mastery-Based Learning” framework, which fundamentally reconfigures the credentialing system through the following structural changes:

1. The Mastery Floor: Employing a 90% comprehension threshold ensures students advance only after demonstrating deep competency, preventing learning gaps from compounding through almost instantaneous feedback when a lack of understanding is evidenced. This is “Competency-based Progression.” (Grading changes: instead of starting at 100% and subtracting for each wrong answer (thus creating negative feedback), MBL starts at 0% and adds to the score, allowing successful answers to reinforce positive feedback.)
2. Competency-Based Progression: Shifting the credentialing framework from a time-based system (calendar-driven) to a competency-based model (demonstrated-skill driven), reinforced with real-time instruction as the topic is fresh and in focus.
3. Variable Time, Fixed Outcomes: Reversing the industrial model of “fixed time/variable outcomes” to a superior “variable time/fixed outcomes” model ensures high-quality learning for every participant. Strategists and Educators must, however, mitigate the “Critical Thinking Paradox.” Students may achieve higher assessment scores with AI assistance while simultaneously experiencing a decline in deep conceptual understanding and emotional IQ. Addressing this risk is a top-tier management priority for institutions seeking to preserve first-principle innovation.

2. Labor Market 2030: Mapping Education to Economic Demand

By 2030, the global labor market could undergo a massive reconfiguration. Estimates suggest that while approximately 92 million jobs could be displaced, 170 million new positions will be created, resulting in a net increase of 78 million jobsⁱⁱ.

This rebalancing will likely shift both hiring volume and categories; demand for workers’ core skills is expected to change by nearly 40% within the decade. Educational institutions must pivot immediately to meet the “velocity of talent demand” or face significant economic misalignment. To support employee profitability in the 2030 economy, educational pathways must align with the following trajectories (below are just a few seemingly obvious examples):

Potentially High-Growth Demand Occupations

• AI and Machine Learning Specialists, Prompting Engineers
• Fintech and Renewable Energy Engineers
• Sustainability Specialists and Strategic Leads
• Nursing Professionals and Secondary Teachers (Driven by demographic shifts in aging and growing populations)

High-Displacement Occupations

• Data Entry Clerks and Bank Tellers
• Graphic Designers and Legal Secretaries (High-level knowledge work being automated by GAI)
• Traditional Manufacturing (non-automated) and Postal Clerks. This labor shift accelerates the credentialing revolution, in which micro-certifications challenge the dominance of the four-year degree.

Hiring Changes will Demand Educational Changes

• Changes to Employer Preference: 74% of employers now favor candidates with verified digital credentials for AI roles over those with traditional degrees alone. This will shift the emphasis from “who you know” or which school you attended to “demonstrated competencies,” while avoiding the legal risks associated with DEI lawsuits.
• Hiring Efficiency: Research shows that skills-based hiring has been shown to reduce time-to-hire by 28% and boost role-match quality by 35%. These economic pressures suggest that the human element becomes the key factor in the transition, especially the teacher’s role.

3. Professional Metamorphosis: Teachers as Learning Architects

The transition to an AI-augmented environment requires a professional metamorphosis for educators, shifting their role from lecturers to “Learning Architects” and “Engagement Coaches.” By automating repetitive administrative and grading tasks, educators can reclaim 20% to 30% of their time, allowing them to focus on mentorship and higher-order cognitive development. The “Human-in-the-Loop” requirement is the strategic pillar of this metamorphosis. The teacher of the future must pivot toward roles AI cannot replicate, focusing on:

• Emotional Intelligence (EQ): Reading subtle social cues and personal circumstances to drive motivation, engagement, and holistic development.
• Targeted Intervention: Using Analytical AI data to provide surgical support for students at risk of “intervention purgatory.” When students lack understanding, immediate intervention accelerates learning and significantly increases understanding and intelligent application.
• Critical Thinking Safeguards: Actively challenging students may help prevent the “Critical Thinking Paradox” mentioned earlier.

Four strategic tasks define the Teacher-AI Collaborative Workflow:

1. Data-Driven Intervention: AI identifies specific learning gaps in real time, allowing the teacher to provide targeted, data-backed support when the topic is fresh and concentration is high.
2. Creative Lesson Design: Using GAI to generate differentiated instruction and custom tests for each student, enabling personalized materials at scale.    
3. Ethical Authority: Educators must serve as the final authority on AI outputs, questioning AI-generated results to ensure that technology does not reinforce bias or undermine the student’s conceptual reasoning.
4. “Technological Self-Efficacy” is vital for teacher retention: educators who feel empowered by these tools report significantly higher professional satisfaction, which is essential as the remote learning ecosystem expands into homes.

4. The Remote Learning Infrastructure: Parental Support and Digital Access

The domestic environment has become a critical strategic node in the educational supply chain. Parental support is no longer a peripheral benefit; it is a foundational requirement for the success of the remote “Tutor Mode.” To meet educational and financial goals, the home must be optimized as a high-fidelity learning site. Parents must provide the Technical Foundations and the discipline required to bridge the “Digital Divide”:

• Reliability: Securing high-speed, consistent internet access as an always-available utility is increasingly important and may be critical to future educational infrastructure.
• Device Management: Providing hardware capable of supporting immersive VR/AR and advanced AI interfaces. These technologies, though still in the lab, will enable education to become “immersive,” potentially leading to additional learning and creative efficiencies.
• Digital Literacy: Developing the family’s ability to navigate AI platforms and evaluate tool effectiveness will be crucial. In remote settings, the “Human Element” is the home’s primary value. While the AI tutor handles technical skills, parents serve as the main facilitators of irreplaceable skills—such as critical thinking, creativity, ethical reasoning, emotional “IQ,” and discipline. These skills must develop outside the digital interface of education. Policy leaders must recognize that parental support is essential for equitable access and that parents and caregivers should be held accountable. Without improving domestic infrastructure, the digital divide will remain a structural economic disadvantage.

5. Economic & Strategic Realities: Scaling the Intelligent Classroom

The economics of education are shifting from high-overhead physical infrastructure to scalable, remote, digital-first models. This transition significantly reduces institutions’ CAPEX, allowing them to redirect capital from physical infrastructure and maintenance to talent development and technology scaling. Research shows that a cost-benefit analysis of “Virtual vs. Traditional” demonstrates a substantial reduction in the cost per student for the intelligent institution.

• 6G technology will enable the strategic delivery of this high-fidelity content. The shift to 6G is a technical prerequisite for immersive learning for several reasons:
  • Sub-millisecond latency is crucial for eliminating motion sickness in VR and enabling real-time haptic feedback in medical and surgical training. It can also enable more remote training, improve understanding and tactile skills, and reduce training costs for education that requires physical interaction.
  • Signal Fidelity: Denoising techniques reduce the Bit Error Rate (BER) from 0.19 to 0.12, ensuring the signal integrity required for complex, high-stakes immersive simulations.
• Looking ahead to 2030, the “Known Unknowns” remain the primary barriers to global leadership. The lack of standardized AI literacy metrics and the rapid pace of talent demand mean that continuous upskilling may become the ultimate competitive advantage in the new economy. Displacement, rather than job loss, will become the reality.
  • This creates a significant unmet educational need for educational institutions. Educational organizations that pursue “wholesale transformative change” rather than incremental use cases could realize the full profit potential of the intelligent era.

As the half-life of skills shrinks, micro-certifications and demonstrated competencies are supplementing traditional four-year degrees.

• Employer Preference: 74% of employers prefer candidates with verified digital credentials for AI roles over those with traditional degrees.
• Skills-First Hiring: This approach reduces time-to-hire by 28% and increases role match quality by 35%.

Summary

The educational system must rethink and reshape itself over the next decade. Sooner rather than later. Looking forward, what is to come?

• Educational institutions are still needed yet must embrace the technologies becoming available today and in the near future (AI, AR, VR, 6G), delivering deeper, faster learning at lower cost with more definable outcomes.
• Teachers’ skills must evolve; Artificial Intelligence must become part of every educational infrastructure. Teachers need to become more like coaches than lecturers. They will spend less time grading papers and more time identifying opportunities to motivate students. “Targeted Intervention” will immediately identify misunderstandings of concepts and applications when the topic is fresh. “Targeted Intervention” increases mastery while reducing costs, ensuring students truly understand and can apply the material.
• Remote delivery of education leading to certification will become commonplace and well received. This will disrupt the hiring process. (See Khan Academy and Alpha Schools for vivid examples of education’s future (today). This Alpha School YouTube segment is a must-watch.)
• Hiring candidates based on merit and skills will become the norm. This will significantly disrupt today’s brick-and-mortar education system. It is likely that “credentialing,” “certification,” and “outcome-based” hiring will become standard, as employers demand the “show me what you can do” hiring method, as opposed to the “who referred you,” “who you know,” or “what school did you attend” process. (What will happen to the brick-and-mortar infrastructure?)
• “Displacement vs. job loss.” The need for “evergreen” education, which enables the workforce to continually upgrade skills, will also become commonplace, creating new opportunities for digitally superior educational facilities.
• The infrastructure required for education will be upended. Vast structural demands will give way to lower-cost, remote facilities. This will significantly affect today’s education system, much as Amazon’s impact on retail infrastructure did. The potential downside of this structural change is that social skills may be less well developed, isolation may become an emotional hurdle, and feelings associated with friendship and bonding could suffer. (However, university infrastructure may have more pickleball courts 😊)

For those interested in investing in the educational category: Jobs with a high probability of existing in 10 years:

• Jobs requiring high-stakes accountability and complex human judgment will remain in demand.
• Human dexterity will also be in demand, along with the soft skills of empathy and emotional accountability.
• Jobs that will survive are most likely to require skills AI can’t deliver, namely those that require empathy, demand high-risk decision-making, creativity, and involve physical dexterity and unpredictability. Examples:

• Healthcare professionals
  • Nurses (master’s, doctoral degree, or national certification)
  • Surgeons (doctoral degrees with 4-8 years of fellowship)
  • Physician assistants (master’s degree and state licensure)
  • Mental health counselors/psychologists/psychiatrists (master’s degree, doctoral degree and/or clinical licensure)

• Engineers and physically unpredictable demands
  • Renewable energy engineers, solar engineers, wind engineers
  • Electricians, plumbers (and all essential infrastructure needing vocational certifications and apprenticeships)

Jobs likely to become obsolete (or lessened) in 10 years:

• Data-heavy tasks (filling in spreadsheets, some coding) and repetitive tasks
  • Administrative and office support jobs, executive assistants, bookkeepers, payroll clerks, data entry clerks
• Transportation and logistics
  • Long-haul truck drivers involved in highway driving (last mile may still require specialized and certified skills
  • Delivery drivers (drones and sidewalk robots)
  • Warehouse workers (robots)
  • Assembly line workers
• Basic content creation and basic research
  • Basic technical writers
  • Proofreaders and copy editors
  • Legal assistants
• Customer service and basic retail employment
  • Cashiers
  • Customer support and client service
  • Telemarketers

Questions remaining and topics not covered or needing more follow-up due to the rate of change:

• Will the U.S. move from a severance-based model (paying severed employees a monetary sum intended to carry them through the job-seeking process) to an educational package designed to reskill the employee relative to the available job market and to the severed employee’s request at the time of severance?
• If yes, will this create a new educational system with an imperative to reskill a displaced workforce and ensure former employees are better equipped for the future? (This type of on-demand education is likely to be credential-based and will reach scale (profitability) only if we move to the MBL and Tutor-Student model. This could be a HUGE market opportunity, creating a huge industry…)

Disclaimer: The materials and topics discussed reflect the views of the Abernathy Group II Family Office and are provided for members of the Family Office community. These topics and materials do not constitute a recommendation or solicitation of any products or services. The information may be based on data from external sources deemed reliable; however, we may not be able to verify it. We make no warranty, express or implied, regarding accuracy or completeness. This material may not be updated in the future. Any opinions and estimates included herein represent our judgment as of this date and are subject to change without notice.

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i Bloom’s 2 Sigma Problem – https://web.mit.edu/5.95/readings/bloom-two-sigma.pdf

ii https://www.weforum.org/media/future-of-jobs-report-2025-78-million-new-job-opportunities-by-2030-but-urgent-upskilling-needed-to-prepare-workforces