It’s Not All Doom and Gloom: Why LLMs Won’t Take Our Jobs

Recently my mother called me after she had watched a YouTube video that talked about how AI
Large Language Models (LLMs) are going to take all of our jobs, leave us all destitute and
upend the whole basis of our society.

While I understood her panic, I assured her that based on my experience as an AI Product Manager working intimately with LLMs, many of her concerns
were overblown. Yes, while some jobs (like note transcribers) are definitely taking a massive
hit, most others are simply going to be augmented by AI and not taken away.

There’s no doubt that LLMs are powerful. They can write essays, draft emails, summarize reports, and even help with coding tasks. But before we assume LLMs are here to replace us all, let’s dig deeper into what these models can and cannot do. Here are several reasons why we’re not heading for a jobless future because of LLMs—and why, instead, they’re more likely to assist and enhance human work than replace it.

LLMs Lack Human Creativity and Originality
While LLMs can generate content based on patterns learned from data, they aren’t creative in
the way humans are. Creativity often involves breaking rules and blending ideas from different
domains, or taking risks that defy established patterns—none of which is a strong suit for LLMs
They work by recognizing and replicating established patterns, which means they can help with repetitive or structured content but are far less effective when innovation or true originality is
required.

For example, while an LLM might write a serviceable business report or technical article, creating an original story, an advertising campaign that strikes an emotional chord, or an inventive business strategy is something only humans are equipped to handle.

Roles that rely on creativity—such as marketing strategists, artists, and product designers—will likely remain firmly in human hands for the foreseeable future.

They Don’t Understand Human Contexts
LLMs can be remarkably good at producing language that sounds human, but that doesn’t
mean they understand what they’re saying in the way that people do. They analyze patterns in
vast amounts of text, yet they lack the cultural, social, and contextual awareness that humans
bring to work. This gap becomes especially apparent in jobs that require empathy, interpersonal
skills, or ethical decision-making

Consider careers like counseling, teaching, and human resources. In these roles, professionals
need to grasp subtle emotional cues, weigh ethical considerations, and provide advice with compassion and empathy LLMs lack the real-world experience, self-awareness, and ethical reasoning needed to replace humans in these fields. They can assist by automating routine administrative tasks, but the nuanced work that relies on human context and empathy remains ours alone.

They Excel in Narrow Tasks, But Lack General Intelligence
LLMs are good at specific tasks like answering questions, generating text, or summarizing
information within a narrow scope. But general intelligence—the ability to understand, learn, and
apply knowledge flexibly across different domains—is still well beyond their reach.

In the workplace, this limitation makes LLMs suitable for assisting with narrowly defined tasks rather
than taking over whole jobs.

Jobs that require adaptability, problem-solving, and the ability to apply knowledge in new or uncertain contexts still heavily rely on human intelligence. For instance, project managers, engineers, and business analysts frequently face shifting project goals, unique challenges, and complex, unstructured problems that LLMs simply cannot tackle with the same effectiveness as humans

Human Judgment and Ethics Are Still Essential
In many professions, judgment calls that involve ethics and morals play a significant role. LLMs,
however, cannot make moral or ethical decisions. They generate outputs based on
patterns without considering consequences, values, or ethical frameworks. As a result, they can’t be relied upon to make decisions where these factors are critical.

Fields such as law, healthcare, and government policy-making, where ethical considerations are
often paramount, will continue to depend on human judgment.

Professionals in these areas need to weigh the implications of their choices, consider long-term impacts, and balance competing values—tasks that are currently well outside the capabilities of any LLM8.

There’s a Growing Need for Human-AI Collaboration
Instead of replacing jobs, LLMs and other AI tools are more likely to augment human work, helping people become more efficient and productive.

The future is likely to see an increase in jobs that require skills in managing and interacting with AI. Many roles will shift focus toward supervision, creativity, and the application of LLM-generated insights.

For instance, content editors can use LLMs to produce initial drafts or brainstorm ideas, freeing
up more time to focus on refining content, ensuring quality, and tailoring materials to specific
audiences.

In customer service, agents can rely on AI to handle routine inquiries while focusing on complex issues that require a human touch. In these ways, human-AI collaboration opens the door to increased productivity rather than job loss

The Economy and Society Need Human Workers
There are social and economic reasons why widespread job replacement by AI may not be
desirable or feasible. Companies and governments alike recognize that sudden shifts toward
automation could lead to societal disruption, with people out of work and consumer spending
dropping—a scenario that ultimately harms businesses as well.

Many companies are therefore
incentivised to integrate AI in ways that support, rather than replace, human workers.

Additionally, governments are likely to regulate AI to prevent significant social upheaval and job
loss. Some countries are already exploring policies that support a “human-first” approach to AI,
encouraging companies to focus on human-AI collaboration models instead of complete
automation.

The Future is Bright for Human Work
While LLMs are set to transform certain types of work, the doomsday scenario of AI taking over
all jobs are largely exaggerated. These models are powerful tools, but they are still just
that—tools. LLMs excel in assisting with repetitive, rule-based tasks, but they lack the depth of
human creativity, empathy, ethical reasoning, and adaptability.

Rather than replacing us, AI offers a valuable opportunity for collaboration, allowing us to offload
mundane tasks and free up time for work that requires human ingenuity and emotional
intelligence. In the long run, LLMs could enhance job satisfaction and open new avenues for
creative, thoughtful, and meaningful work. The future of work isn’t “doom and gloom”—it’s a
promising partnership where humans and AI can thrive together.

References
Simon, H. A. (1995). Artificial Intelligence: The very idea. MIT Press.

Amabile, T. M., & Pratt, M. G. (2016). The dynamic componential model of creativity and
Innovation in organizations: Making progress, making meaning. Research in Organizational Behavior, 36, 157-183.

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

Fischer, S., & Wertz, J. (2017). AI and Empathy: Can Artificial Intelligence Help People
Better Understand Human Emotions? Technology and Empathy Journal, 4(2), 45-62.

Tegmark, M. (2017). Life 3.0: Being human in the age of artificial intelligence. Knopf.

Muller, V. C., & Bostrom, N. (2016). Future progress in artificial intelligence: A survey of
Expert opinion. In Fundamental Issues of Artificial Intelligence (pp. 555-572). Springer,
Cham.

Johnson, D. G. (2015). Ethics and technology: Controversies, questions, and strategies
for ethical computing.Cengage Learning.

Moor, J. H. (2006). Why we need better ethics for emerging technologies. Ethics and
Information Technology, 8(4), 277-283.

Levy, F., & Murnane, R. J. (2012). Dancing with robots: Human skills for computerized
work. Third Way NEXT Report, 5-17.

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

Davenport, T. H., & Ronanki, R. (2018). Artificial intelligence for the real world. Harvard
Business Review, 96(1), 108-116.

Frey, C. B., & Osborne, M. A. (2017). The future of employment: How susceptible are
jobs to computerization? Technological Forecasting and Social Change, 114, 254-280.

Newlands, M., & Fallon, N. (2021). Future Proofing your workforce: How companies are
Investing in employees alongside automation. Business Insider Intelligence, 3(1), 23-31.