How LLM adoption has impacted AI job roles?

Something that exists as an abstract idea in the mind takes only a few seconds to become tangible today. Be it something as creative as an image or a poem or something too technical like a spreadsheet macro or even lines of codes, anything can be generated almost instantly with the right prompt.

All thanks to the LLM-powered phenomenon we commonly call Generative AI. While there is the awe factor and fascination surrounding its capabilities, there’s also a parallel debate and fear on its occupation of existing jobs.

There is skepticism around a lot of jobs succumbing to Gen AI & automation and more becoming obsolete. But like how everything has the right, the wrong, and the truth, I felt it was important to address the truth here.

So, here’s a deep-dived approach to how the adoption of LLMs has impacted AI job roles, broken down across key aspects.

The Current Landscape of the AI Talent Market

The Gen AI market is currently valued at around $13 billion and by 2025, it is expected to reach a value of $22 billion, indicating incredible growth. This steady growth simultaneously leads to skyrocketing of the demand for AI talent. However, what is going south is the supply of skilled professionals in the Gen AI space.

Traditional roles in AI - data scientists and machine learning engineers, remain highly sought after, but companies are finding it difficult to retain the talent. As a result, the industry is witnessing a shift in focus towards more advanced AI technologies, specifically Large Language Models (LLMs).

Evolution of Traditional AI Job Roles

To understand the impact of LLM adoption, it’s vital that we get a sense of how AI job roles have been evolving conventionally.

Initially, AI professionals were primarily engaged in developing algorithms, coding, and optimizing models. With AI technology maturing, these roles expanded to include tasks like data preprocessing, feature engineering, model deployment, and ML OPs. Interestingly, we observe that the demand for soft skills has gained momentum, along with domain expertise, like communication skills to bridge the gap between technical teams and business stakeholders.

Impact of LLM Adoption on AI Job Roles

The widespread adoption of Generative AI and LLMs has introduced a paradigm shift in the AI job market. Since the requirements are becoming more niche, it’s simultaneously giving rise to specific skills and competencies.

As a result, newer roles have emerged, such as: AI language model trainers, responsible for fine-tuning models to suit specific business needs; Ethical AI specialists are now in high demand to ensure responsible and unbiased use of AI technologies; AI linguists play a crucial role in refining language models to better understand and generate contextually relevant content and more.

The integration of LLMs has not only broadened the spectrum of AI job roles but has also deepened the collaboration between AI experts and professionals from other disciplines.

Emergence of Novel AI Job Roles

As we just saw, the rise of LLM adoption has given birth to roles that were once considered unconventional in the AI domain. Creativity has become an indispensable aspect of Gen AI, where AI storytellers and creative AI developers are now essential for crafting compelling narratives and designing AI systems that can generate creative content.

AI trainers are playing a pivotal role in continuously improving language models through iterative learning processes. These emerging roles highlight the dynamic nature of the AI job market and the need for a diverse skill set beyond traditional technical competencies.

ML Ops has become a critical role to finally deploy and bring the LLM into action within the products and applications. This in itself is a dramatic shift from the conventional DevOPs.

Future Trajectory of AI Professionals and the Industry

As AI technology continues to advance, the demand for professionals with expertise across several LLMs, along with other cutting-edge technologies will soar. AI professionals will need to adapt to evolving roles, emphasizing creativity, ethical considerations, and interdisciplinary collaboration.

The industry is likely to see increased specialisation, with professionals focusing on niche areas such as AI interpretability, explainability, interoperatability between models and AI-driven decision support.

With Generative AI graduating from AGI (Artificial General Intelligence) to ASI (Artificial Super Intelligence), advanced skill upgradation is needed. Continuous upskilling and a holistic understanding of business processes will be crucial for AI professionals to stay relevant and contribute meaningfully to the ever-expanding field of artificial intelligence.

From an enterprise perspective, it boils down to the nurturing of talent with an approach that has a sentiment stemming from outcome-competency mapping. 

Vinay Konanur

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Vinay Konanur is Vice President – Emerging Technology at UNext Learning.