Pre AGI Capabilities and AI in the Work Field

Abstract

My research focuses on how artificial intelligence is developing very quickly and moving closer to artificial general intelligence, or AGI, which could change the way people work in many careers around the world. In my paper, I look at the current growth of AI, how it may affect different jobs in the future, and whether high school students are prepared for a workforce shaped by these changes. I review current research on AI's impact across different professions, and I also use an ongoing survey to better understand how aware students are and how prepared they feel. From the research I have looked at so far, I have found that AI probably will not completely replace most jobs, but it will likely become important in almost every career field. Because of this, I believe people will need to understand how to use AI and how to work with it effectively. Based on my literature review, I argue that schools should start teaching AI literacy and help students see AI not just as something that replaces workers, but as a tool that can support human abilities and improve the way people work. My ongoing survey will also help me understand what students currently know and what kinds of educational changes may be needed.

Introduction

The rise of artificial intelligence is shaping up to be one of the biggest technological changes in human history. Unlike past industrial revolutions, which mostly automated physical labor, AI is now beginning to take on tasks that involve thinking, problem-solving, and creativity, which were once seen as skills only humans could do. I believe this is a major shift, and it raises important questions about the future of jobs, career planning, and how students should prepare through education.

Today's AI systems are already very advanced in many areas. They can understand language, recognize images, help diagnose medical conditions, write software, and even create art. As AI continues moving toward artificial general intelligence, or AGI, where it may be able to perform across many fields like a human, I think it is important to ask which jobs will remain, how those jobs may change, and what skills will still matter in a world where AI is part of everyday life.

In my research, I explore these questions by reviewing existing studies and collecting new information through an ongoing survey. My research suggests that the strongest path forward is not to view AI as a replacement for people, but as a tool that can work alongside humans. I also study high school students' career goals and how aware they are of AI's growing impact. This helps me understand how prepared students currently are and what kinds of changes in education may be needed for the future.

Current State of AI Technology

Modern artificial intelligence has achieved capabilities that were considered theoretical only a decade ago. Large language models such as GPT-4 demonstrate sophisticated natural language understanding and generation (OpenAI, 2023). Computer vision systems match or exceed human performance in specific medical imaging tasks (McKinney et al., 2020). AI systems now assist in complex decision-making across fields from financial trading to drug discovery.

AI has been improving at a rapid rate. Sevilla et al. (2022) showed that the computing power used to train AI has been growing exponentially; basically doubling every six months. This rapid progress means AI systems are getting better at more general tasks, not just specific narrow applications.

The Path to Artificial General Intelligence

Artificial General Intelligence refers to AI systems capable of understanding, learning, and applying knowledge across any intellectual task performable by humans. Expert predictions regarding AGI timelines vary considerably, however the consensus acknowledges its theoretical possibility and potentially near-term realization.

Research from OpenAI and the University of Pennsylvania suggests that large language models already show some early forms of general reasoning, though they still have significant limitations (Eloundou et al., 2023). Major AI researchers like Geoffrey Hinton have publicly warned about both the amazing potential and serious risks of AGI development (Taylor, 2023). Ngo (2023) argues that AGI might arrive faster than people historically predicted, which means we need to start planning for it now.

Economic Impact of AI on Employment

Multiple economic analyses have attempted to quantify AI's impact on labor markets. Goldman Sachs projects that AI could affect 300 million full-time jobs globally, with approximately two-thirds of current occupations in developed economies experiencing some degree of AI integration (Hatzius et al., 2023). However, these projections emphasize transformation rather than complete elimination of roles.

Brynjolfsson et al. (2023) conducted empirical research on generative AI's impact on worker productivity, finding that AI-assisted workers completed tasks 12.2% faster with 40% higher quality outcomes. This research suggests augmentation rather than replacement as the primary near-term effect. However, the study also indicated that effectiveness required workers to develop new skills in AI collaboration.

Frey and Osborne (2017) estimated that 47% of U.S. employment faces high risk of automation within two decades. Their analysis identified routine cognitive tasks and predictable physical work as most vulnerable. However, subsequent research by Acemoglu and Restrepo (2020) provides a more nuanced view, suggesting that while AI displaces certain tasks, it simultaneously creates demand for new complementary tasks requiring human judgment.

Task-Based Analysis of AI Impact

Modern economic research increasingly focuses on task-level rather than occupation-level analysis. Eloundou et al. (2023) found that approximately 80% of the U.S. workforce could have at least 10% of their work tasks affected by large language models, with higher-wage occupations generally facing greater exposure. This finding challenges assumptions that only low-skill work faces automation risk.

The World Economic Forum's Future of Jobs Report (2023) projects that by 2027, 23% of jobs will change through growth of 69 million new roles and decline of 83 million roles. Significantly, the report identifies AI and machine learning specialists as the fastest-growing occupation, alongside roles requiring human-AI collaboration such as data analysts, business intelligence analysts, and digital transformation specialists.

Human-AI Collaboration Research

Emerging research emphasizes collaborative frameworks over replacement models. Wilson and Daugherty (2018) found numerous cases where human-AI collaboration outperformed either humans or AI working independently across medical diagnostics, business forecasting, and customer service. Their research identified three categories of emerging jobs: trainers who teach AI systems, explainers who interpret AI decisions, and sustainers who ensure AI systems operate ethically.

Mollick and Mollick (2023) demonstrated that students using AI assistance for business tasks showed significant performance improvements, but only when they developed beneficial collaboration strategies. This finding suggests that simply having access to AI tools provides limited benefit without corresponding skill development in effective use.

Skills for the AI Era

Multiple research institutions have identified skill sets that remain valuable alongside AI advancement. The Organisation for Economic Co-operation and Development emphasizes creativity, critical thinking, emotional intelligence, and complex communication as significantly important (OECD, 2019). These capabilities complement rather than compete with AI strengths in data processing and pattern recognition.

Brynjolfsson and McAfee (2014) argue that future economic value will accrue to individuals who can combine technical literacy with uniquely human capabilities. Their research suggests that education systems must shift from information transfer to development of adaptability, creativity, and interpersonal skills that enable effective collaboration with increasingly capable AI systems.

Methodology

To assess current student preparedness for an AI-integrated workforce, this research employs a survey methodology targeting high school students across multiple grade levels and diverse career interests.

Survey Design

A structured survey has been developed for high school students containing three key questions:

Career intention identification: "What is your intended major or future career?"
Self-assessed AI understanding: "On a scale of 1-5, how well do you understand whether your future career will require AI skills?"
Perceived AI dependency: "Do you believe your future job will be more dependent on AI skills or traditional human skills?"

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Survey Goals

The survey aims to reach high school students representing diverse academic interests and grade levels. The goal is to gather insights into student awareness of AI's impact on their intended careers, identify knowledge gaps, and understand perceptions about the future of work in an AI-integrated economy.

Data collected through this ongoing survey will help inform educational institutions, policymakers, and students about the current state of AI literacy and career preparation among high school students.

Research Objectives

This survey-based research aims to investigate several key areas regarding high school students' preparedness for AI-integrated careers:

AI Understanding Assessment

The study will examine student self-assessment of their understanding regarding AI's impact on their intended careers. This will help identify whether students have realistic awareness of how AI technologies will affect their chosen professions.

Career Field Variations

Analysis will explore whether understanding varies across different career fields (STEM vs. non-STEM, creative fields, healthcare, etc.) and whether technology exposure correlates with AI awareness.

Perception of AI vs. Human Skills

The research will document student perceptions about whether their future careers will depend more on AI skills or traditional human skills, and compare these perceptions with current research literature on AI integration in various professions.

Qualitative Insights

Open-response sections will reveal common themes in student reasoning, including assumptions about human irreplaceability, awareness of current AI capabilities, understanding of human-AI collaboration models, and whether students have considered AI's impact on their career planning.

Participate in This Research: Help contribute to our understanding of student preparedness by completing the survey. Your responses will inform recommendations for educational institutions and policymakers.

Take the Survey Now

View Current Results

Analysis of AI Impact by Career Field

Research literature enables classification of careers along a spectrum of AI integration, from high automation risk to human-AI collaboration to primarily human-centered roles.

High Automation Risk (60-90% of tasks potentially automated)

Occupations in this category involve primarily routine cognitive or physical tasks with predictable patterns. These include data entry, basic customer service, simple bookkeeping, routine manufacturing, and telemarketing (Frey & Osborne, 2017). McKinsey estimates that current AI technology could automate 50% of work activities in these fields within the next decade (Manyika et al., 2017).

Significant AI Integration Required (30-60% of tasks affected)

This category encompasses professional roles where AI can perform substantial portions of current tasks but human oversight, creativity, or interpersonal skills remain essential. Examples include:

Medical diagnostics: AI analyzes imaging and suggests diagnoses, but physicians make final determinations and communicate with patients
Legal services: AI conducts document review and legal research, but attorneys handle strategy and client relations
Financial analysis: AI processes market data and identifies patterns, but human analysts make investment decisions
Software development: AI generates code and identifies bugs, but human developers design architecture and solve novel problems
Journalism: AI writes routine reports, but human journalists conduct investigations and provide analysis
Graphic design: AI generates design options, but human designers refine concepts and ensure brand alignment

Collaborative Human-AI Roles (10-30% of tasks affected)

These careers involve complex decision-making, interpersonal relationships, or physical work in unpredictable environments. While AI provides decision support, humans maintain primary responsibility. Examples include advanced surgical specialties, scientific research, complex engineering projects, educational instruction, senior management, and psychotherapy.

Primarily Human-Centered (0-10% of tasks affected)

Certain occupations maintain predominant human control due to requirements for physical presence in variable environments, deep interpersonal trust, or societal preference for human interaction. These include skilled trades, direct patient care, early childhood education, high-stakes negotiation, and performance arts.

The Case for Human-AI Collaboration

Research evidence increasingly supports collaborative frameworks over replacement models. Several factors drive this conclusion:

Complementary Capabilities

AI systems excel at pattern recognition, data processing, and consistent execution of defined tasks. Humans excel at contextual understanding, ethical reasoning, creative problem-solving, and interpersonal communication.

Economic Incentives

Organizations adopting AI seek competitive advantage, not workforce reduction per se. Research indicates that workers who effectively utilize AI tools provide greater value than workers replaced by AI.

Technical Limitations

Despite rapid progress, current AI systems lack common sense reasoning, struggle with truly novel situations, and cannot reliably explain their decision-making processes.

Social and Ethical Considerations

Many decisions require ethical judgment, accountability, and trust that society may not delegate to AI systems regardless of technical capability.

Survey Focus Areas

This survey will collect data on several key questions to understand student preparedness:

AI Understanding: How well do students understand AI's potential impact on their chosen career fields?
Career Perceptions: Do student beliefs about AI's role in their careers align with current research literature on AI integration?
AI as Tool vs. Threat: How do students view AI—as a collaborative tool or as a replacement threat?
Career Planning: Have students considered AI's impact when making educational and career decisions?

Survey results will be analyzed and published as data is collected. Visit the results dashboard to view current findings in real-time.

Preparing for an AI-Integrated Workforce

Based on the literature review of AI's impact on various career fields, the following guidance outlines how different stakeholders can prepare for workforce transformation. The ongoing survey will help determine which of these areas require the most urgent attention in educational settings.

For Students

Develop AI Literacy: Seek understanding of AI capabilities, limitations, and applications in your field of interest
Acquire Hybrid Skills: Combine domain expertise with AI collaboration skills
Cultivate Human-Distinctive Capabilities: Focus on creativity, critical thinking, emotional intelligence, and ethical reasoning
Maintain Adaptive Mindset: Develop comfort with ongoing skill acquisition rather than expecting stable career trajectories
Research Career-Specific AI Impact: Investigate AI applications specific to your intended career

For Educational Institutions

Integrate AI Literacy Across Curriculum: AI education should not be confined to computer science courses
Update Career Counseling: Include realistic discussion of AI's impact on different professions
Model Effective AI Collaboration: Educators should demonstrate appropriate AI tool usage in instruction
Emphasize Transferable Skills: Focus on adaptability, critical thinking, and learning capacity
Provide Hands-On Experience: Students should work with AI tools in educational contexts

For Policymakers

Educational Standards Reform: Incorporate AI literacy and human-AI collaboration skills as learning objectives
Teacher Professional Development: Educators need training in both using AI tools and teaching about AI effectively
Curriculum Development Support: Create age-appropriate AI education materials across subjects
Ethical Framework Development: Address ethical considerations of AI including bias, privacy, and accountability

The Importance of Understanding Student Preparedness

As AI continues to transform the workforce, it is critical to understand how well students are preparing for AI-integrated careers. This research seeks to examine the relationship between student awareness of AI's impact and their career planning decisions.

The literature reviewed in this paper demonstrates that AI will affect virtually all career fields to varying degrees. Whether students recognize this reality and are taking steps to prepare accordingly remains an open question that this survey aims to answer.

Furthermore, how students conceptualize AI—whether as a collaborative tool that augments human capabilities or as a threat that replaces human workers—may significantly influence their educational choices and career preparation strategies. Understanding these perceptions is essential for informing educational policy and curriculum development.

Ongoing Research: This survey continues to collect data from high school students. Results will be analyzed and updated regularly. View the live results dashboard to see current findings as they emerge.

Findings

As artificial intelligence becomes more advanced, it will likely affect almost every career field by changing the skills people need rather than fully replacing most jobs. I believe many high school students are still not fully prepared for these changes and may not yet realize how important AI literacy will be in their future careers.

The results of my research support this idea. In the studies I reviewed, AI is already being used in many fields, including medicine, business, technology, education, and the arts. Although AI can complete certain tasks quickly and efficiently, most careers still depend on human skills like decision-making, creativity, communication, and ethical judgment. My survey research also helps show how students currently think about AI, what careers they are interested in, and how aware they are of AI's growing impact on the workplace. Altogether, these findings suggest that AI is becoming an important part of many professions and that students will need to be prepared to work with it.

Overall, my research suggests that the future of work will most likely involve collaboration between humans and AI, not total replacement. Even though AI can perform many tasks, it cannot fully replace the human strengths that many careers still require. Because of this, future workers will need both strong human skills and the ability to use AI effectively. If students are not introduced to these ideas early, they may be less prepared for the workforce they will enter. This is why education will likely need to adapt by teaching AI literacy and helping students understand AI as a tool that can support human work rather than simply replace it.

References

Acemoglu, D., & Restrepo, P. (2020). Robots and jobs: Evidence from US labor markets. Journal of Political Economy, 128(6), 2188-2244. https://doi.org/10.1086/705716

Bostrom, N. (2014). Superintelligence: Paths, dangers, strategies. Oxford University Press.

Brynjolfsson, E., & McAfee, A. (2014). The second machine age: Work, progress, and prosperity in a time of brilliant technologies. W.W. Norton & Company.

Brynjolfsson, E., Li, D., & Raymond, L. (2023). Generative AI at work. National Bureau of Economic Research Working Paper No. 31161. https://doi.org/10.3386/w31161

Eloundou, T., Manning, S., Mishkin, P., & Rock, D. (2023). GPTs are GPTs: An early look at the labor market impact potential of large language models. arXiv preprint arXiv:2303.10130. https://doi.org/10.48550/arXiv.2303.10130

Frey, C. B., & Osborne, M. A. (2017). The future of employment: How susceptible are jobs to computerisation? Technological Forecasting and Social Change, 114, 254-280. https://doi.org/10.1016/j.techfore.2016.08.019

Harari, Y. N. (2018). 21 Lessons for the 21st century. Spiegel & Grau.

Hatzius, J., Briggs, J., Kodnani, D., & Pierdomenico, G. (2023). The potentially large effects of artificial intelligence on economic growth. Goldman Sachs Economics Research.

Manyika, J., Lund, S., Chui, M., Bughin, J., Woetzel, J., Batra, P., Ko, R., & Sanghvi, S. (2017). Jobs lost, jobs gained: Workforce transitions in a time of automation. McKinsey Global Institute.

Marcus, G. (2020). The next decade in AI: Four steps towards robust artificial intelligence. arXiv preprint arXiv:2002.06177. https://doi.org/10.48550/arXiv.2002.06177

McKinney, S. M., et al. (2020). International evaluation of an AI system for breast cancer screening. Nature, 577(7788), 89-94. https://doi.org/10.1038/s41586-019-1799-6

Mollick, E. R., & Mollick, L. (2023). New modes of learning enabled by AI chatbots: Three methods and assignments. Available at SSRN 4300783. https://dx.doi.org/10.2139/ssrn.4300783

Ngo, R. (2023). AGI safety from first principles: Introduction. Alignment Forum.

OECD. (2019). OECD Employment Outlook 2019: The future of work. OECD Publishing. https://doi.org/10.1787/9ee00155-en

OpenAI. (2023). GPT-4 technical report. arXiv preprint arXiv:2303.08774. https://doi.org/10.48550/arXiv.2303.08774

Sevilla, J., Heim, L., Ho, A., Besiroglu, T., Hobbhahn, M., & Villalobos, P. (2022). Compute trends across three eras of machine learning. arXiv preprint arXiv:2202.05924. https://doi.org/10.48550/arXiv.2202.05924

Taylor, J. (2023, May 2). 'Godfather of AI' Geoffrey Hinton quits Google and warns over dangers of misinformation. The Guardian.

Wilson, H. J., & Daugherty, P. R. (2018). Collaborative intelligence: Humans and AI are joining forces. Harvard Business Review, 96(4), 114-123.

World Economic Forum. (2023). Future of Jobs Report 2023.

Pre AGI Capabilities and Analysis on AI in the Work Field

How Will Humans be Affected?