In this article I want to specifically focus on how artificial intelligence might affect the job market in the coming years. Both directly, in industries working with these technologies, as well as indirectly as potential tools for recruiters and jobseekers.<\/p>\n\n\n\n
Creative Destruction<\/h2>\n\n\n\n
A lot of discussions about AI focus on what job sectors are most at risk of being replaced, or which workers are most likely going to become redundant through AI-driven automation. What often falls to the wayside in these discussions, is that AI technology will also help to create new jobs and economic niches.<\/p>\n\n\n\n
I found it helpful to think of this through the lens of \u201ccreative destruction\u201d. While there is no single origin for the concept, its most closely associated with 20th<\/sup> century political economist Joseph Schumpeter. The idea of \u201ccreative destruction\u201d assumes that change is inevitable, and that established economic processes must be destroyed to make room for newer, improved processes. This kind of change and creative destruction are continuously driven by innovation and are hallmarks of capitalist society. One very approachable example of this process is the rise of Netflix and the on-demand streaming industry, which has put most brick-and-mortar video rentals out of business.<\/p>\n\n\n\n You can visualise artificial intelligence as the driver of a similar process: through innovation and the availability of new technologies some jobs will stagnate and perhaps disappear entirely. But it will also allow for the creation of entirely new and exciting job profiles and industries, as well as ways of integrating these technologies into wider society. \u00a0<\/p>\n\n\n\n But what does this \u201ccreation\u201d mean in the context of AI specifically?<\/p>\n\n\n\n There are many reports trying to pinpoint which industries and job categories are most at risk, or most likely to benefit, from AI augmentation. One such example is this paper<\/a> from the World Economic Forum, specifically examining the potential impact of large language models (LLM).<\/p>\n\n\n\n The associated summary article identifies three categories of direct beneficiaries when it comes to LLM adoption: trainers, explainers, and sustainers.<\/p>\n\n\n\n \u201cTrainers\u201d are those specialists who work on the development and maintenance of AI technology and systems, such as AI engineers or system administrators. The category of \u201cexplainers\u201d covers more user-facing interaction, such as interface or functionality design of these new technologies. The third category are the \u201csustainers\u201d, who ensure that the technology gets used appropriately \u2013 this might cover anyone from ethics and governance specialists to data curators. <\/p>\n\n\n\n Essentially, these categories stand in direct contact with new technology: developing it, rendering it usable and valuable to both trained and untrained users. Quite some of these jobs already exist, but if AI adoption continues at a steady rate, they will likely see a lot of demand in near future.<\/p>\n\n\n\n But if we expect widespread adoption of AI-powered tech, what changes can we expect outside of the AI industry bubble?<\/p>\n\n\n\n I have discussed various use cases for generative AI on this blog before, especially with an eye towards freelancers. There are certainly some clients and employers who strongly oppose the use of generative AI programmes among their employees and freelancers. Maybe this will become a more common stance in the future \u2013 or perhaps such sentiments will die off as generative AI becomes more normalised?<\/p>\n\n\n\n On the other side of that are freelancers (and regular employees as well) who use generative AI to optimise or speed up their projects. Especially in recruitment AI might also become a game changer. It might allow recruiters to comb through more profiles, automating tasks such as basic outreach to potential candidates. This is a recurring pattern in many use cases for AI \u2013 it would allow for the automation of repetitive or time-consuming tasks, freeing us up to focus on things which require human nuance, creativity, or judgement.<\/p>\n\n\n\n Even jobseekers might begin to harness AI to optimise their search \u2013 perhaps to draft out cover letters and more easily identify jobs they are qualified for. Maybe we will see a sort of arms race between recruiters and candidates, each trying to maximise the value of AI to streamline their process?<\/p>\n\n\n\n AI Will Radically Reshape Job Market, Global Economy, Employee Productivity<\/a><\/p>\n\n\n\n Artificial Intelligence Jobs: How Will AI Change The Job Market?<\/a><\/p>\n\n\n\n Creative Destruction: Out With the Old, in With the New<\/a><\/p>\n\n\n\n How to use generative AI in business | Hibernian Recruitment<\/a><\/p>\n\n\n\n Jobs AI will create? Here\u2019s the World Economic Forum view | World Economic Forum<\/a><\/p>\n\n\n\n Jobs of Tomorrow: Large Language Models and Jobs | World Economic Forum<\/a> <\/p>\n\n\n\n <\/a>What will AI do to the freelancer market? | Hibernian Recruitment<\/a><\/p>\n\n\n\n <\/a>What\u2019s an AI Engineer? Understanding Jobs in AI | Hibernian Recruitment<\/a><\/p>\n\n\n\nTrainers, Explainers and Sustainers<\/h2>\n\n\n\n
![\"\"](\"https:\/\/www.hibernian-recruitment.com\/wp-content\/uploads\/2023\/10\/wef-llm-jobs-1024x850.jpg\")
Harnessing Artificial Intelligence<\/h2>\n\n\n\n
Sources<\/h2>\n\n\n\n