How Will Generative AI Revolutionize Our Work?

Bram Verhoef, Head of Machine Learning & Co-Founder at AXELERA AI shares his thoughts.

This week was Labor Day, a day dedicated to celebrating the achievements and perseverance of the workforce. Now we find ourselves on the cusp of a new era where artificial intelligence (AI) is poised to transform the labor market. The dawn of this technological revolution holds both promise and peril and as we gather to honor the labor and perseverance of preceding generations, we, as an AI company, find it crucial to ponder the implications of AI for the future of work. Let us delve into the profound impact generative AI may have on employment, exploring both the opportunities it creates and the challenges it presents, as we strive to shape a world where human ingenuity and machine intelligence can coexist harmoniously.

What is generative AI?

Moravec’s Paradox is a principle in artificial intelligence that highlights the observation that tasks that are relatively easy for humans to perform can be quite difficult for machines, while tasks that are hard for humans can be relatively simple for machines. Previously, activities like playing chess and complex arithmetic calculations were effortlessly handled by computers, while object recognition, language understanding, and walking remained elusive for machines. However, with the emergence of deep learning, AI systems have made remarkable advancements in tackling tasks that were once considered human domain, such as image recognition and natural language processing. Although not all seemingly “easy” tasks have been conquered by AI, the advancements in deep learning over the past decade have undeniably brought machines closer to mastering human-centric tasks. This holds especially true for generative models.

Generative models represent a class of machine learning algorithms that surpass the constraints of decision boundaries, enabling the capacity to model the intrinsic properties of data distributions. As such, generative models overcome the limitations inherent in discriminative models. In contrast to discriminative models, which prioritize delineating decision boundaries between data distributions—like discerning between images of dogs and cats—generative models encapsulate the inherent structures and patterns within the data distributions themselves.

The capacity of generative models to capture the intrinsic structure of data is far from trivial. The complexities of data distributions, such as images and text, are vast and high-dimensional, encompassing rare examples that cannot be overlooked (i.e., long tails). However, recent advancements in deep learning techniques have served as a catalyst, unlocking a plethora of opportunities in this domain. Propelled by large datasets (e.g., the internet), sophisticated models (e.g., transformer blocks), increased computing resources (e.g., dedicated AI accelerators), and innovative learning techniques (e.g., reinforcement learning from human feedback), generative models are now making waves across the digital landscape.

Today, generative models astound us with their capacity to generate hyper-realistic images through technologies like those employed by Stable Diffusion [1], DALL-E [2], and Midjourney [3]. Meanwhile, their linguistic siblings, such as GPTs [4] and LLaMA [5], produce human-like text that defies expectations. Video creation is revolutionized with Video LDM [6], and speech synthesis achieves new heights of authenticity [7].

Large Language Models (LLMs) like ChatGPT are particularly impressive, demonstrating proficiency in a wide array of tasks, including text summarization, general question-answering, music composition, code writing, mathematical problem-solving, and even understanding human intentions (theory of mind) [8]. Groundbreaking innovations like Auto-GPT [9] and BabyAGI [10] push the envelope further, imbuing LLMs with self-prompting, memory capabilities, browsing, and critical reasoning. Unlike traditional chatbots, auto-GPT and BabyAGI may operate with minimal human intervention, edging us ever closer to the domain of Artificial General Intelligence (AGI).

As the rapid evolution of generative AI reshapes our world, the impact of these transformative techniques on the labor market emerges as a significant consideration. Both the positive and negative consequences merit our attention as we contemplate how generative AI will redefine our professional landscape and what this revolution means for the future of work. How will generative AI reshape our professional landscape?

Impact of Generative AI on the Labor Market

The concept of machines encroaching upon our jobs is far from novel. Throughout history, innovations such as steam-powered machines, computers, and robots have simultaneously captivated and alarmed us with their potential impact on the labor market. In the near future, we may well equate AI’s role in the fourth industrial revolution to that of the steam engine in the first. Should AI progress maintain its current trajectory, its substantial influence on employment is all but certain. Consequently, identifying the occupations most vulnerable to AI disruption is crucial for adapting to and capitalizing on this technology.

OpenAI, the very organization responsible for ChatGPT, has explored the possible effects of LLMs on the U.S. labor market. They discovered that roughly 80% of jobs could have at least 10% of their tasks impacted by LLMs [11]. Moreover, 19% of jobs might experience 50% of their tasks being affected. As anticipated, the occupations facing the greatest impact are those heavily dependent on writing and computer programming. A corroborating study by Goldman Sachs reveals that approximately two-thirds of occupations are susceptible to some degree of AI disruption, with a quarter of jobs potentially having up to half their workload replaced [12]. The study also forecasts that administrative and legal jobs will be among those most significantly affected by AI [Figure 2].

Figure 2. Share of industry employment exposed by automation by AI [12].

In contrast to previous waves of automation, manual occupations in sectors like manufacturing, construction, agriculture, and mining are predicted to be less affected by this emerging technology. This is primarily due to the current disparity in advancement between data-driven AI and robotics. The OpenAI study also anticipates a lesser impact on scientific occupations and jobs demanding critical thinking. Nevertheless, recent research involving GPT3.5 and GPT4 has already demonstrated how LLMs could potentially streamline and expedite scientific endeavors [14].

General-purpose technologies, such as printing and the steam engine, are typified by their widespread diffusion, ongoing enhancement, and the generation of complementary innovations [11]. The aforementioned studies suggest that AI will be a general-purpose technology. Importantly, such technologies also present considerable opportunities for growth and development.

Opportunities of Generative AI

Over the course of history, employment displacement caused by automation has consistently been counterbalanced by the creation of new job opportunities. This phenomenon is attributable to the advent of new occupations that arise in response to technological innovations, which account for the lion’s share of long-term employment growth. A recent study by economist David Autor revealed that 60% of the present-day workforce is engaged in professions that did not exist in 1940. This implies that a staggering 85% of employment growth in the past 80 years can be ascribed to the creation of new positions driven by technological advancements [12].

A myriad of novel job opportunities is already making its presence felt. Examples include prompt designers who fine-tune prompts for optimal performance of AI based on LLMs, companies that create fashion designs using image generators like Stable Diffusion, and firms that boost productivity by providing real-time suggestions by LLMs for how to respond in customer support [13]. It has been estimated that as natural language processing advancements permeate businesses and society, they hold the potential to trigger a 7% (or nearly $7 trillion) surge in global GDP and elevate productivity growth by 1.5 percentage points over a decade [12].

Figure 3. Resleeve AI Fashion Design Image [15].

In the past, several concerns have been raised by various authors regarding the potential exacerbation of social and economic inequality due to automation. They argue that automation could eventually give rise to a “useless class” of economically irrelevant individuals, while high-skilled workers and those possessing the means of production amass increasing wealth [16]; [17]. Such concerns have predominantly centered on the ramifications of automation for blue-collar occupations. However, the studies mentioned above suggest that generative AI may disproportionately impact white-collar jobs, with many blue-collar jobs remaining largely unaffected. Moreover, AI could provide opportunities to those who currently lack them, serving as a source of knowledge and intelligence that empowers individuals with less developed digital skills to bring their ideas to fruition. Consequently, if AI is democratized effectively, it also holds the potential to reduce social inequality.


Groundbreaking discoveries by Nobel laureates Daniel Kahneman and Amos Tversky have illuminated the dual nature of human cognition, encompassing two interrelated subsystems known as System 1 and System 2 [18]. System 1 operates rapidly, intuitively, and automatically, with minimal conscious effort. Governed by heuristics and shaped by experience, it is also the origin of numerous cognitive biases. In contrast, System 2 is characterized by a deliberate, analytical approach to thinking, responsible for logic, reason, and complex problem-solving.

Historically, early AI research focused on tasks primarily associated with System 2, as exemplified by symbolic AI. The advent of deep learning (and machine learning in general) shifted the focus toward emulating System 1, with recent advances in AI beginning to merge both systems. This convergence paves the way for a more comprehensive form of artificial cognition, often referred to as artificial general intelligence (AGI). As AI continues to evolve, however, humans face its progress with both excitement and fear. Crucially, both emotions are instrumental: the former creates a window through which we can see opportunities, while the latter causes us to think critically about how we will integrate this technology in our society.

With large language models (LLMs) and other generative AI systems poised to impact numerous professions, some jobs may be facilitated or transformed, while others may vanish entirely, and new roles will emerge in their stead. Nonetheless, the unpredictable and nonlinear nature of AI’s rapid progress and its extensive applicability suggest that we are dealing with a “Black Swan” event—a rare occurrence with large unforeseeable consequences that could be both positive and negative [19]. Analogous to the Industrial Revolution, the advent of electricity, or the rise of the internet, the long-term effects of AI remain currently opaque.

 Nevertheless, the influence of AI is unmistakable. In response, it is crucial to welcome the changes ushered in by AI and harness its power, particularly in professional contexts. As we celebrate Labor Day, let us embrace adaptation and innovation in response to this revolutionary technology. Let us weave AI into the fabric of our professional pursuits, transforming our everyday endeavors for a brighter future. And let us strive to democratize AI, ensuring its benefits are accessible to all. Happy Labor Day.


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