Artificial Intelligence (AI) is transforming our conversations about the future of work, with intensifying debates about how AI will reshape employment, skills and knowledge, labor markets, workplace dynamics, and the policies that govern them. This post is the first in a series titled “AI and the Future of Work,” hosted on the Notre Dame-IBM Technology Ethics Lab’s blog.
In this series, we will examine the implications of AI for jobs and society: how tasks and roles might change, how organizations can adapt, how workers are or might be affected, and what governance frameworks are being considered to create and support beneficial and responsible outcomes. Before we dive into those topics in future entries, it’s crucial to establish a clear foundation of what AI actually is – and what it is not. Hype and misconceptions about AI abound, coupled with the use of “AI” as an umbrella term for a multitude of different technologies, can cloud our understanding and hinder effective decision-making. This opening article will therefore define key AI concepts in accessible terms, dispel common myths, and discuss why an accurate understanding of AI matters for the future of work.
A balanced, realistic perspective on AI (neither alarmist nor techno-utopian) is essential in order to harness AI’s benefits while safeguarding ethical and social values in the workplace. Let’s begin by unpacking what we really mean by “AI” in 2026.
Defining AI: Generative, Agentic, and General Intelligence
AI is not a monolithic entity or a single technology; rather, it encompasses a broad range of techniques and systems that can perform tasks typically requiring human intelligence. These tasks might include recognizing patterns, making predictions, or even creating novel content. However, not all AI is the same. It’s important to differentiate between narrow AI systems designed for specific functions and the aspirational concept of general AI. Below, we define and discuss three key categories that often come up in conversations about modern AI:
Traditional and Foundational AI
Before turning to generative AI, agentic AI, and AGI, it is important to situate these developments within the much longer history of artificial intelligence. AI did not begin with large language models or autonomous agents. Since the mid-20th century, researchers have developed a range of foundational AI techniques, including rule-based systems, machine learning, statistical modeling, computer vision, natural language processing, and optimization algorithms. These systems underpin many of the most effective and widely deployed AI applications today, from fraud detection and medical image analysis to recommendation systems and logistics planning. Crucially, contemporary generative and agentic systems are not replacements for these earlier forms of AI; they are built on top of them. IBM mentions that many traditional AI approaches remain better suited for specific tasks, often requiring fewer computational resources and offering greater reliability and interpretability.
In what follows, this section focuses on the forms of AI that have attracted the most attention in 2026, while acknowledging that they rest on—and coexist with—older, well-established AI technologies that continue to play a central role in practice.
Generative AI: Machines That Create
One of the most talked-about developments in recent years is generative AI. Generative AI refers to systems that can produce new content such as text, images, music, or code. In older technologies, a model might learn to make a prediction or classification (for example, determining if an X-ray image shows a tumor). By contrast, generative AI models are trained on a vast amount of data and are able to generate new data that resemble the patterns in the training set. In simple terms, rather than just analyzing or predicting existing data, a generative AI system creates original output. For instance, OpenAI’s ChatGPT can write an essay or answer a question with human-like fluency, and image generators like DALL-E can produce artwork based on text prompts. These systems learn the statistical structure of language or images or audio files from millions of examples, then use the patterns learned during training to synthesize new paragraphs, pictures, videos, or music that look and/or sound coherent and realistic.
How does generative AI work under the hood? Many of today’s generative models, especially for text, are based on large language models (LLMs). These are deep neural networks trained on enormous amounts of human-written text. The training process teaches the model to predict the next word in a sentence, which in turn enables it to generate entire paragraphs of text. As a result, tools like ChatGPT can carry on conversations, write code, or compose articles by essentially predicting one word at a time. For images, techniques such as diffusion models and Generative Adversarial Networks (GANs) are used. These models can start from random noise and gradually refine it into a coherent image, or use a pair of networks in competition to produce life-like images. The core idea is the same: generative AI systems learn the patterns of the training data and use those patterns to produce new content that mimics the originals.
It’s worth noting that while generative AI’s capabilities seem astonishing, the technology builds on decades of prior research. Early forms of generative models, like simple Markov chains used for text autocompletion, have existed for many years. The recent “boom” in generative AI was enabled by advances in model architecture, computational power, and data availability that dramatically increased the complexity and realism of what machines can generate.
Agentic AI: AI with (Limited) Autonomy and Agency
Another term gaining attention is agentic AI, which refers to AI systems capable of autonomous planning and action. While generative AI focuses on content creation, agentic AI is about perceived or programmed “agency”. An agentic AI system can be described as autonomous software that acts independently to achieve predetermined objectives. Traditional software will only do exactly what it is explicitly told, step by step, waiting for another user or system to give it the next command. By contrast, an agentic AI can be proactive: it doesn’t just react to a single prompt and stop, but can continue to operate, monitor its environment, and adjust its actions in pursuit of its goal.
To illustrate, consider a task like managing an office building’s climate control for energy efficiency. A non-agentic system might follow a fixed schedule or simple if-then rules set by programmers. An agentic AI system, however, might continually monitor conditions, predict usage patterns, and autonomously adjust settings to save energy without a human explicitly specifying every action.
A related concept is multi-agent systems. Rather than relying on a single agent, these systems involve multiple AI agents that interact, coordinate, or divide tasks among themselves to achieve a broader objective. Each agent may have a specialized role (for example, one agent gathering information, another making decisions, and a third executing actions), and the system’s behavior emerges from their interaction. While multi-agent systems are not new in computer science, their combination with modern generative and agentic AI has renewed attention, particularly in workplace contexts. Much of the current discourse around the “future of work” builds on this idea, suggesting scenarios in which teams of agents could perform complex organizational tasks, or where human workers increasingly supervise, coordinate, or manage groups of AI agents rather than execute every task directly.
It’s important to stress that current agentic AI systems are still limited in scope. They operate within predefined domains and goals set by humans. The “agency” they have is bounded by their programming and training. What differentiates agentic AI from simple automation is the level of decision-making complexity and adaptability. These AI agents can handle unexpected situations by drawing on machine learning models. For example, if a delivery robot (an embodied AI agent) encounters an obstacle, it can re-plan its route on the fly. Or, as AWS describes, if an employee calls in sick, an agentic scheduling system could independently communicate with other employees and rearrange shifts to fill the gap.
Artificial General Intelligence (AGI): The Grand Ambition
Looming behind many AI discussions is the concept of Artificial General Intelligence (AGI). Unlike the previous two categories, AGI does not refer to a technology we have today, but rather a theoretical future AI that possesses broad, human-level cognitive abilities. In other words, an AGI would not be limited to a specific domain or task as it would be capable of understanding, learning, and applying knowledge across a wide range of tasks equivalent to a human intellect. Sometimes called “strong AI” or “general AI,” AGI implies a machine intelligence that could perform any intellectual task that a human can, exhibiting common sense, reasoning, and adaptability in unfamiliar situations.
It’s important to clarify that AGI remains hypothetical in 2026: no such system exists, and experts disagree on how long it might take to achieve (if it’s achievable at all). Major AI research organizations have stated long-term missions to pursue AGI, which has fueled excitement and speculation. However, as of today, we have only Artificial Narrow Intelligence, meaning AI that excels in specific areas but lacks general-purpose understanding. Even the most advanced generative models are considered narrow AI because they operate within a constrained mode (such as generating text) and do not possess a truly generalized understanding or autonomous will.
By defining these terms, we establish a vocabulary for more nuanced discussion. Generative and agentic AI are cutting-edge developments within the realm of narrow AI that we see emerging in workplaces and society today. AGI is a broader concept on the horizon that often enters conversations about “AI taking over” or surpassing human capabilities, but it is crucial not to conflate AGI with what current systems can do.
Related is the idea of artificial superintelligence. While AGI refers to a system with roughly human-level, general cognitive abilities, superintelligence is typically used to describe a hypothetical AI that would surpass human intelligence across most or all domains, including scientific reasoning, creativity, and strategic decision-making. Some researchers and technology leaders view superintelligence as a long-term ambition beyond AGI, while others express concern about its potential risks and societal consequences. Superintelligence is even more speculative than AGI: it does not exist today, and its feasibility, timeline, and governance remain deeply contested.
Next, we turn to some widespread misconceptions about AI’s capabilities and impacts, to further clarify what AI is not.
What AI Is Not: Dispelling Common Misconceptions
Despite (or perhaps because of) all the attention on AI, a number of myths and misconceptions have taken hold in public discourse. Here we address a few of the most common misunderstandings about AI, especially as they relate to jobs and autonomy, and provide a more accurate picture based on current evidence and expert insight.
“AI will replace all jobs.”
Myth: A frequently voiced fear is that AI and automation will soon render human workers obsolete across most industries, leading to mass unemployment. Sensational headlines often claim a huge percentage of jobs will disappear.
Reality: AI is transforming many jobs, not eliminating them. While it’s true that AI can automate certain tasks within jobs, it does not mean entire occupations will vanish overnight. A recent McKinsey Global Institute report, for example, was widely misinterpreted as saying “40% of jobs will be lost to AI.” In fact, the report found that 40% of jobs have a significant portion of tasks that could be automated. Many roles will be redefined rather than destroyed. History shows that technology tends to automate specific work activities while also creating new jobs or new industries (for instance, the rise of software development as a career, or the need for AI ethicists and data curators today).
Many predictions for the coming decade reflect job transition rather than apocalypse. The World Economic Forum’s Future of Jobs Report 2025 projects that while 85 million jobs may be displaced by automation by 2025, about 97 million new roles may emerge, especially those involving human skills and oversight of AI. In net terms, they anticipate job growth, not decline, but with a shift in the skill sets required. The key takeaway is that AI will likely automate certain routine or repetitive tasks.
“AI is fully autonomous and doesn’t need humans.”
Myth: Some popular portrayals give the impression that today’s AI systems can learn, act, and improve entirely on their own, without human involvement.
Reality: No current AI operates in a vacuum. Human input and oversight are indispensable at every stage. Far from being independent minds, today’s AI systems are created, trained, and maintained by humans, and often require continuous human guidance. An apt reminder of this reality is the fact that each query you pose to an AI like ChatGPT “draws on countless hours of human input” used to train and fine-tune the model. Behind the scenes, humans have curated training data, written algorithms, and in many cases are still in the loop, testing and fine-tuning the models, correcting AI outputs, or handling exceptions. Today’s AI has no independently determined intentions of its own; it works within parameters and commands set by people. Overestimating AI’s autonomy can be dangerous, as it may lead to insufficient human oversight, over-reliance, or unnecessary fear.
“Current AI is essentially like human intelligence (or an early form of AGI).”
Myth: Because modern AI can perform impressively in certain domains, some assume that these systems are on the verge of human-like general intelligence. It’s a common confusion to think that if an AI is smart in one way, it’s generally smart in the way a person is. This misconception blurs the distinction between narrow AI and the hypothetical general AI.
Reality: Even the most advanced AI today is narrow, not general. As discussed earlier, AI programs excel at specific tasks or types of tasks, leveraging patterns in data, but they do not possess a holistic or situated understanding or consciousness. For example, the fact that GPT-4 can solve math problems or draft legal contracts does not mean it has reasoning abilities comparable to a human lawyer or mathematician; it means it has seen many documents and examples and can mimic the patterns of reasoning found in those texts. IBM’s AI research division succinctly notes that Artificial Narrow Intelligence is the only form of AI that exists today, and that includes everything from Siri to IBM’s own Watson to OpenAI’s ChatGPT. These systems operate within the bounds of their training and programming. They lack the open-ended learning and adaptable problem-solving that a human (or a theoretical AGI) has. Furthermore, no current AI system can transfer its “knowledge” from one domain to a vastly different one the way humans regularly do. A child who learns a game of logic can apply reasoning skills from that game to a school puzzle or a life problem, but an AI trained to play StarCraft at superhuman level cannot play chess unless it’s retrained from scratch on chess data. Each AI system is an isolated specialist.
With these misunderstandings addressed, we see that much of the dramatic narrative around AI is misplaced. However, misconceptions about AI are not just innocent mistakes; they can have significant consequences. In the next section, we explore why it matters that we accurately understand what AI is and isn’t, especially as we make decisions about the future of work.
Why Misunderstanding AI Poses Risks (and Getting it Right Matters)
In the context of the future of work, perhaps the most immediate impact of misunderstanding AI is felt in the workplace and business decisions. Employers might implement AI tools based on inflated expectations, and employees might be subject to policies or pressures stemming from those misconceptions. A recent study of companies adopting AI found that initial productivity often drops because organizations underestimate the need for training, process re-engineering, and oversight when introducing new AI tools. In some extreme cases, middle managers have reported feeling pressured to “prove” AI’s value even when the tools underperform, essentially pretending that AI systems are flawlessly handling work that still actually requires significant human fixing. This phenomenon was observed in interviews with managers who felt they had to uphold an “AI-optimal” image to satisfy higher-ups; they ended up doing extra work behind the scenes and attributing it to AI to maintain the illusion, leading to stress and burnout.
Moreover, misunderstanding AI can harm worker morale and agency. Fear-driven narratives about AI taking all jobs can demoralize employees, making them feel their skills, knowledge, and experiences are about to be rendered useless when in reality many of their abilities remain uniquely valuable. On the other hand, treating AI as infallible can lead companies to trust algorithmic decisions (hiring, firing, performance evaluations) without proper human review, which raises serious ethical concerns.
In light of these considerations, deploying AI responsibly in the workplace is an urgent task for all stakeholders. For the public, it means approaching AI news and claims critically, neither falling for hype nor giving in to fear, but asking “what can this AI really do?” and “what are its limits?” For policymakers and leaders, it means grounding strategies in facts on the ground and focusing on how AI is actually being used (or misused) in workplaces, and where interventions are needed to protect people and promote innovation. For businesses and managers, it means adopting AI mindfully: implementing new tools with careful change management, training, and a backup plan when AI outputs need human correction. It also means designing AI with a human-centered philosophy, where the goal is not to replace humans, but to augment human capabilities: making work more fulfilling, enabling tasks to be completed more efficiently, and freeing up time so people can enjoy more of their lives outside of work. As technology ethicists Arvind Narayanan and Sayash Kapoor argue in their book AI Snake Oil, it’s crucial to understand what AI can and can’t do so that organizations don’t fall for solutions that sound too good to be true.
Finally, for workers and communities, understanding AI provides critical information that can be used to advocate for how AI is introduced in their workplace, to demand transparency, and to push for training opportunities to work effectively with AI tools. Human insight, expertise, and trust will remain essential in effectively deploying AI. In fact, as AI grows more powerful, the importance of human judgment, ethics, and oversight only increases.
Final Thoughts: Building a Grounded Perspective on AI and Work
In this first entry of the "AI and the Future of Work" series, we set out to demystify what AI is – and is not – to ensure that subsequent discussions rest on solid ground. We learned that generative AI systems can create astonishing text and images, yet they operate by learning patterns rather than thinking like a human. We discussed agentic AI as an active area of AI research and design where systems gain a degree of autonomy in executing tasks, while noting that true independence or general reasoning is beyond their reach (and human oversight remains key). We clarified that Artificial General Intelligence, the idea of human-level machine intellect, is a theoretical goal on the horizon, not a reality today.
Addressing these definitions allowed us to confront popular misconceptions. The fear that “AI will steal all jobs” was tempered by evidence that AI is more about task automation and job transformation than wholesale replacement of workers. Why do these distinctions matter? Because how we conceive of “AI” will shape how we respond to it. In the workplace, misunderstanding AI can result in poorly implemented systems, dashed expectations, harm to employee well-being, or neglect of the human workforce that quietly supports AI. By contrast, a realistic, informed perspective on AI’s capabilities and limits helps ensure that we maximize the technology’s benefits (productivity gains, augmentation of human work, new creative tools) while responsibly managing its drawbacks (displacement of certain tasks, need for retraining, ethical use of algorithms).
As we proceed in this blog series, this grounded understanding of AI will serve as our compass. The future of work in the age of AI is neither a dystopia of mass unemployment nor a utopia of effortless abundance. It is a future we must build with care, knowledge, and a clear-eyed view of the role of humans and technology. AI, in the end, is a set of tools (remarkably powerful tools, but tools nonetheless) shaped by human choices. By knowing what AI is and what it is not, we empower ourselves to harness these tools for good: to enhance productivity, to generate new opportunities, and to elevate the quality of work. Equally, we prepare ourselves to mitigate risks: to ensure that the rise of AI does not widen inequality, erode dignity, reduce critical thinking, or undermine the value of human labor.
Originally published by at ethics.nd.edu on March 03, 2026.