LLMs will Transform the Future of Work

LLMs will Transform the Future of Work

As generative AI introduces a new paradigm of collaboration between humans and AI, it will redefine how work is conducted and reshape the nature of various job roles

The rapid advancement of technology has always been a double-edged sword. While it brings about efficiency and innovation, it also raises concerns about job displacement and the future of work. One such technological marvel that has recently caught the world’s attention is the Large Language Model (LLM). These models, backed by generative artificial intelligence (AI), are not just changing the way we interact with information but are also reshaping the global job landscape.

A Paradigm Shift in Interaction and Work

Generative AI, especially LLMs, are at the forefront of a paradigm shift. They have the capability to create original content, generate insights from vast amounts of data, translate languages with near-human accuracy, and even make complex decisions. Such versatility and efficiency could have profound implications for jobs and the future of work.

However, with great power comes great responsibility. While the application of LLMs could lead to significant productivity gains and the creation of new types of jobs, there is also a risk that they could displace existing roles, exacerbating socioeconomic disparities and creating a sense of job insecurity among the global workforces. As such, integrating AI into our workplaces is a balancing act between seizing opportunities and managing potential disruptions.

23% of jobs will be transformed by AI

Recent advancements in LLMs – such as GitHub’s Copilot, Midjourney, and ChatGPT –are expected to cause significant shifts in global economies and labour markets. The World Economic Forum’s Future of Jobs Report 2023 predicts that 23% of global jobs will undergo transformation in the next five years due to factors like AI. To understand the potential impact of LLMs on jobs, an analysis was conducted on over 19,000 individual tasks across 867 occupations. The findings were revealing:

  • Jobs at High Risk: Roles like Credit Authorisers, Checkers and Clerks, Management Analysts, and Telemarketers have a high potential for automation by LLMs.
  • Jobs for Augmentation: Some jobs, such as Insurance Underwriters and Mathematicians, have a high potential for task augmentation, emphasising mathematical and scientific analysis.
  • Jobs with Lower Risk: Occupations like Educational Guidance, Career Counsellors, and Clergy have a lower potential for automation or augmentation, indicating that they might remain largely unchanged.

Moreover, the adoption of LLMs is expected to pave the way for new roles, including AI Developers, Interface and Interaction Designers, AI Content Creators, and AI Ethics and Governance Specialists.

Industry-wise Analysis

When the potential exposure levels of jobs were aggregated to the industry level, financial services, insurance and pension management emerged as the industries with the highest potential exposure to LLMs. They were closely followed by IT and digital communications, and then media, entertainment, and sports.

The Emerging Jobs

As generative AI introduces a new paradigm of collaboration between humans and AI, it will redefine how work is conducted and reshape the nature of various job roles. No predictions can be 100% certain regarding which new roles may appear with the widespread adoption of LLMs. Still, it is apparent that there is room for job development in several key areas.

The following illustrative groupings of emerging jobs can help unlock the value of generative AI and mitigate associated consequences:

  • AI Model and Prompt Engineers: Engineers and scientists will continue developing and fine-tuning LLMs at the most detailed level of AI systems innovation. Some of the skill-sets in these jobs may already exist, but they will continue to evolve simultaneously with AI systems progress. These jobs cover the range of programmers designing more efficient algorithms, electrical engineers designing custom chips to train and run the models, systems administrators building server infrastructure, and infrastructure and power systems engineers ensuring these systems have the stable energy sources needed for extended runs. In addition, Prompt Engineers will be critical to developing, refining and reframing prompts or inputs for LLMs to reach more optimal results.
  • Interface and Interaction Designers: Completed and trained LLMs are still highly technical and will require well-crafted interfaces to be accessible to the public. In some ways, Interface and Interaction Designers can be considered user experience (UX) designers for LLMs. This family of jobs will be responsible for crafting LLMs to adapt to a particular kind of user input (for example, typing or spoken voice) or to perform particular tasks, such as in the development of personalised AI assistants, tutors or coaches. These jobs could include the important stage of reinforcement learning with human feedback (RLHF), in which models are trained on favoured responses and other performance evaluators.
  • AI Content Creators: Building off the infrastructure of Technologists and Interface Designers, AI Content Creators will harness the knowledge and understanding of LLMs to rapidly produce in-depth content on a topic in any field or domain. The type of content produced could vary from articles and books to teaching and training material to entire storylines for movies and television series, potentially automatically generating any accompanying visual and audio media.
  • Data Curators and Trainers: Massive training data sets are integral to maintaining the performance of LLMs. Ensuring high-quality data is a priority in LLM development, as the quality of an LLM’s output directly reflects the quality of its training data. As most training data are curated from text posted to the internet, data quality and integrity checks are critical and will lead to the development of a dedicated, specialised workforce.
  • Ethics and Governance Specialists: The presence of prejudiced or other unsavoury language in training data can lead LLMs to produce biased, harmful or unethical content. Not only will training data need to be checked for quality, but trained LLM systems will need to be rigorously tested before being released to the public. This will fall into the purview of specially trained AI Safety Officers and Ethicists at the company level and even domain-specific regulators and lawyers at the government level. AI Content Creators will harness the knowledge and understanding of LLMs to rapidly produce in depth content on a topic in any field or domain.
  • Jobs of Tomorrow: While the emergence of new job categories can be expected, the reinvention of existing roles should also be anticipated. Analysis of the impact of LLMs on customer service jobs found that – of the 13 core customer service tasks – four tasks remained unchanged and within human capabilities, four tasks could be fully automated using generative AI, five tasks could be augmented to enhance human performance, and five new high-value tasks emerge.With generative AI, Customer Service Representatives (CSRs) can engage in new tasks like providing feedback for system improvement, aligning with customer needs, testing for biases and ensuring ethical machine behaviour, and monitoring data privacy. These responsibilities empower CSRs to shape AI deployment, optimise customer experiences and uphold ethical standards in customer service operations. Additionally, another study of CSRs found that the implementation of generative AI was associated with lower employee turnover. These findings demonstrate how organisations could use generative AI alongside human expertise to rethink job design, enhance productivity and improve employee experience.

The Road Ahead

The findings of the report shed light on how the implementation of LLMs could alter the job landscape. Rather than leading to outright job displacement, LLMs may usher in a period of task-based transformation of occupations. This calls for proactive strategies by policymakers, educators, and business leaders to prepare the workforce for the jobs of tomorrow.

While the future might seem uncertain, one thing is clear: the integration of LLMs into our workplaces is a balancing act. By understanding and addressing the direct disruptions, organisations can harness the power of LLMs to enhance productivity, unlock new opportunities, and ensure a smooth transition for their workforce.

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