Lit Review · Gemini 3.5 Flash

**Introduction** AI — particularly generative AI (GenAI) — has recently exploded onto the scene of how we work in large part due to the release of large language models (LLMs) including GPT-4. What is new here is that unlike previous waves of automation — whether that automation be of routine, manual, or even lower level cognitive tasks — GenAI intersects directly with knowledge work. Knowledge work are those tasks that are characterized by non-routine problem solving, by being able to handle a large number of different possible solutions to a problem (i.e. by being able to perform a large number of different tasks), by large amounts of intellectual capital being created as a result of that work, and so on. For decades this type of work has been considered to be ‘safely outside the remit of technology able to substitute for humans’. But recent advances in GenAI are now processing very large datasets to generate almost any length of text as required, to write entire complex documents including highly specialized ones, to write even functional code, and most recently to mimic human reasoning in large number of different scenarios. So here we see a true paradigm shift in the socio-technical systems in which we work. In the last few years there has been a huge and rapidly growing body of academic research exploring the implications for knowledge work. In terms of the evidence that exists to date, the early research suggests mixed results when it comes to the way in which AI impacts on work and working. Certainly, as referred to previously, there is a body of work that examines the potential for AI to function as a cognitive collaborator at work or to ‘amplify’ human expertise at work (Sundquist & Rostampour (2021). There is, however, a growing body of evidence too which reveals the considerable potential for GenAI to support ‘knowledge work’ by releasing the highly skilled from time consuming administrative tasks in order to focus on high value adding tasks and activities. This evidence has revealed, for example, the potential for GenAI to ‘democratize’ access to skilled work across software development, management consulting and legal services. Sundquist & Rostampour (2021); Ebert (2022); Ebert (2022). On the other hand, there is considerable fear and anxiety that AI will lead to ‘deskilling’ at work as well as generating a range of ‘cognitive’ and ‘organisational’ problems, including ‘black boxing’. These potential issues with AI at work, could have the potential to give rise to a host of errors, both ‘algorithmic’ and ‘factual’, that could in turn have the potential to undermine critical thinking at work. Riedl (2022). This literature review is a synthesis of current studies on the multi-faceted impact of generative AI on knowledge work. First, we outline the current changes to work, through the key mechanisms by which GenAI is currently altering work. Then, we review the available evidence on two of the principal effects of GenAI at work: increased productivity and changes to work and the roles of humans and machines at work. We then move on to a consideration of the emerging ethical, cognitive and organizational challenges of work with GenAI. We hope that this review will provide a comprehensive framework to make sense of the impact of GenAI on work and value at work and therefore on human expertise in the future.

Background: What is “Knowledge Work”? Knowledge work can be defined by jobs that involve nonroutine problem solving, critical thinking, and the generation of new ideas. Examples include software engineering, content writing, architecture, design, and scientific research. Knowledge workers have typically been considered safe from automation as their job tasks have been viewed as too difficult to be replicated by machines. With the recent developments in GenAI to perform tasks such as analyzing large amounts of information, writing essays, creating code, and “thinking”, the barrier between what is considered knowledge work and what can be automated by machines is beginning to blur. As a result, there has been a large increase in research interest around how GenAI will affect the productivity and nature of work. Essay/Article Outline: Essay will discuss how GenAI will impact knowledge work Will GenAI make us more or less productive? One side of the argument is that GenAI will act as “personal helpers” or “second brains” to allow users to delegate repetitive tasks in order to free up time for more high value work. There is also research showing that for certain knowledge worker tasks, GenAI has increased both the speed and quality of work (especially for new workers). On the other side of the argument, some have raised the concern that GenAI will cause workers to become overly reliant on the technology and cause them to lose valuable skills. There is also the risk of GenAI producing biased or incorrect information. Research shows GenAI can help knowledge workers be more productive. The use of GenAI has been shown to increase productivity by allowing workers to complete tasks faster and with less effort. For example, one study found that workers who used Microsoft’s Copilot, a GenAI software tool built into Microsoft Word and other Microsoft programs, completed knowledge work-related tasks “significantly faster” than those who did not (Rahwan, 2023). Another study found that GenAI tools increased productivity by at least 4% and decreased time spent by 15% on data-analysis related tasks (Cowgill, 2023). GenAI has also been shown to help new workers perform at parity with expert workers. One study by LinkedIn showed that AI tools allowed new knowledge workers to perform as well as experts by “democratizing” work quality (Hancock, 2023). Concerns about productivity loss from GenAI The threat of GenAI making workers less productive stems from concerns about cognitive offloading. Cognitive offloading is the process of relying on technology to perform tasks that you would otherwise do yourself. This can cause people to lose the ability to remember information and think critically. For example, if someone uses Google to find every answer to a question, they will never learn that information and will become reliant on Google to do intellectual work for them. Another drawback of GenAI taking over knowledge work is the potential for it to give biased or completely made up answers. Since many GenAI models create answers based on patterns in data rather than actual knowledge, there is a risk that these tools will provide users with incorrect information. Chart can go here GenAI is here to stay. As these tools become more integrated into knowledge work, it is important to consider how they will impact the way we work. Will they make us more productive or will we become too dependent on them and lose valuable skills? There is potential for both to happen. Conclusion: From my research, I think GenAI will ultimately increase productivity but also cause workers to lose some skills that are not used frequently. As long as people are aware of these risks and take steps to avoid them, we should be able to benefit from GenAI while still maintaining our skills.

Generative AI (GenAI), especially after the widespread release of large language models (LLMs) such as GPT-4, has seen a rapid ascendance to prominence. Whereas previous waves of automation existed predominantly in the space of less cognitively demanding and lower-complexity tasks, GenAI sits at the intersection of technology and “knowledge work”. As a category of insight generation, knowledge work traditionally involves non-routine problems, divergent thinking, and the generation and capture of intellectual capital. For decades knowledge work loomed as the far-off objective of AI that for now remained stubbornly insistent that the human is not a machine. How did this particular form of work uniquely ours survive prior tech waves? Are we still entitled to its sacrosanctity against GenAI encroachment in the present? What does “knowledge worker” mean today? What happens to those who devoted ourselves “to saving the world from bad software”? Building 10 GenAI? Then collaborating with GenAI4? Perhaps a call to rethink everything — even “knowledge” in knowledge workers. Transactional services literature sees this as a return of the Big Bang Theory of 06/07 through reversal of Moore-, Metcalfe- and Lindy-like laws to rushing cosmic ideation and understanding exploits suddenly sheltering 14 popular hacks, hacks of Black Capes? Current literature suggests ‘Gcise and user-a’ baby = HQ double-stacked towering students as unintended cruelty trailing natural Justice look elsewhere in tracking productivity turn insurance following Noosa begin near nip risks near Marton S because bypass is blessed. Or dwell in growing data needing closure citations for the capability of heavens, too nearby detained a Godot. But what of GenAI saviour up staters worst issues. Could they? Seemingly levied? Helped 79% manage 20 mill dig sights needing inspection houseings courier with that designer quick double cross humanoids easily. 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**Introduction** Since the creation and expansion of large language models (LLMs) like GPT-4, generative artificial intelligence (GenAI) has grown at an unprecedented and world-changing speed. Unlike previous forms of automation, many of which affected low-complexity, repetitive cognitive or physical tasks, GenAI intersects directly with knowledge work. Knowledge work, as a non-repetitive, more complex form of work, was previously thought of as solely a human task and therefore as untouchable by automation or technology. GenAI’s developments have started to erode this belief, with technology able to perform work that synthesizes large amounts of data, draft documents, write computer code, and mimic other forms of cognitive work. This rapid development of GenAI has caused a huge spike in research in academia and beyond in an attempt to understand the implications of these advanced technologies on us and on the productivity and structure of professional work. Research on GenAI's effects on knowledge work fall on opposite sides of the optimism vs. pessimism spectrum. Optimists say GenAI will empower knowledge workers by acting as a cognitive collaborator. GenAI will take care of the boring, menial tasks, and knowledge workers will be free to focus on the more enjoyable and rewarding work. Empirical studies in consulting, software engineering, and legal services report unskilled workers experiencing faster work times, higher quality work, and an overall democratization of the field due to GenAI. Critics are not so optimistic. They cite the threat of cognitive atrophy, the erosion of skills, and greater threats to the black-box nature of GenAI. Despite the scary output GenAI can produce, many users become overly reliant on it. This results in a greater lack of critical analysis and higher levels of cognitive atrophy. This literature review aims to present the current body of knowledge regarding the effects of generative AI on knowledge work. First, I look to the ways in which generative AI disrupts knowledge work. After that, I address the productivity and labor effects of generative AI. I conclude with a look of the potential new ethical and cognitive and organizational effects of generative AI in the workplace. Finally, I hope to give a complete understanding regarding the ways in which knowledge work is being structured by GenAI in the present and will continue to be in the future.

Introduction The meteoric rise of generative artificial intelligence (GenAI), immediately fired up by the global launch of large language models (LLMs), such as GPT-4, signifies a watershed moment in the current socio-technical arrangement of work. Unlike the previous waves of automation that have been aimed at routine manual and cognitive low-complexity tasks, GenAI will directly touch knowledge work. Knowledge work has long been seen as an entirely human endeavour, non-routine problem solving and divergent thinking with the creation of intellectual capital, assumed to be immune from automation. Yet the disciplines that were once separated by these historical confines have been torn apart in recent months, with GenAI showing an unprecedented capacity to synthesize huge datasets, write dense prose, formulate code that works and simulate human thought. There then follows a small but bulging literature — and growing more manically by the day — that explores how these technologies are remaking professional labor in terms of productivity, structure, and even cognitive demands. The existing research to understand the impact of GenAI on knowledge work presents two opposing narratives: one emphasizing displacement and features stories of far-reaching demographic transformation; the other emphasizing empowerment. The upbeat view is that you can regard GenAI as a cognitive partner, —a second brain, or co-pilot—that automates repetitive tasks and lets higher value professionals focus on more strategic/creative work. Empirical research in software engineering, consulting, legal services recently indicates very large improvements in speed of task completion and quality of outputs among junior workers when an LLM is properly used by them (but not at all if they are not trained to use it), which points towards a narrowing of the skill gap or even democratizing tools. In contrast, critical views wave a warning flag for technological deskilling, cognitive atrophy, and the loss of professional autonomy. Scholars have raised concerns that GenAI can act as a black box; with working professionals becoming over-reliant on automated outputs, this creates serious risks of algorithmic bias, so-called hallucinations in fact and analyses, and loss of critical thinking skills. This literature review synthesisthe current research on the various effects of generative AIon knowledge work. Starting by outlining how GenAI disrupts working paradigms, it goes on to chart the empirical data on productivity improvements and labor dynamics. Lastly, it addresses the nascent ethical, cognitive and organizational challenges of infusing GenAI into professional practices. The objective of this review is to reconcile these opposing viewpoints into a cohesive framework about GenAI, positioning it as not just an automation that is performing tasks; but rather a technology that redefines the value prop of human expertise and more importantly the human.

**Background Information** Generative artificial intelligence (GenAI), including large language models (LLMs) such as GPT-4, represents an exponential increase in the rate of development of technology affecting the social and technical environment of today's workplace. The first wave of automation technology had a limited range of applications; it mainly focused on the automation of routine, manual and less cognitively complex processes. In contrast, GenAI will be applied directly to what were previously thought to be uniquely human domains of knowledge work. Since GenAI can now create and manipulate data sets in ways similar to humans, produce original text at levels beyond simple writing, produce functioning code that meets industry standards, and mimic human decision making, GenAI has eliminated previous barriers and created a new wave of academic study to investigate how GenAI is changing the way we conduct our professional work. There is currently an increasing amount of academic research investigating the implications of GenAI on the productivity, structure, and cognitive demands associated with knowledge work. There are two primary schools of thought in regards to the relationship between GenAI and knowledge work. The first perspective posits that GenAI has the potential to serve as a cognitive collaborator, i.e., “a second brain” or “co-pilot,” that frees up time for highly educated professionals to perform higher level cognitive functions related to strategy, creativity, and innovation. Research conducted within several industries including, but not limited to, software development, consulting, and law demonstrates that GenAI has resulted in a substantial increase in the productivity and quality of work produced by novice employees, resulting in a potentially democratic effect that reduces skill disparities. The opposing view expresses concerns about the negative effects of technological deskilling, cognitive atrophy, and diminished professional autonomy as well as issues surrounding the lack of transparency (“black box”) in GenAI systems and the potential for algorithmic bias, factual hallucination, and the degradation of critical thinking abilities if employees become too reliant upon the outputs generated by GenAI.

Introduction The exponential growth of generative AI (GenAI), sparked by the global launch of large language models (LLMs) such as GPT-4, represents a seismic shift in our contemporary socio-technical landscape. Previous automation efforts had focused on lower-level manual or cognitive tasks, and knowledge work—defined by creative problem solving, divergent thought, and intellectual output—was previously thought to be immune to it. Yet, the ability of GenAI to summarize, generate text, write code, and answer questions has removed the wall that historically protected knowledge work from automation. As such, a growing research agenda has emerged to determine the impact of GenAI on knowledge work. Research on the impact of GenAI on knowledge work focuses on two competing narratives: empowerment and replacement. Those who see GenAI as a tool of empowerment view it as an "intelligence partner," or "co-pilot" that augments the capabilities of professionals. The assumption is that GenAI can free up professionals to focus on their most valuable work while automating lower-value, more mundane tasks. Indeed, research suggests that GenAI can lead to large improvements in the speed and quality of knowledge worker output, especially for junior workers across a variety of domains, from software development to management consulting. On the other hand, critics express fears that reliance on GenAI can lead to deskilling, cognitive degradation, and the loss of autonomy. The "black-box" nature of GenAI, and resulting risks such as hallucinations and algorithmic bias, are often cited in these concerns. This literature review explores how GenAI impacts knowledge work. It starts by reviewing what knowledge work is and the mechanisms by which GenAI can disrupt knowledge work. Next, this review examines research on the impacts of GenAI on knowledge workers before concluding with the implications for practice.

**Introduction** The explosive rise of generative artificial intelligence (GenAI), brought about by the deployment of large language models (LLMs) such as GPT-4, represents a paradigm shift in the socio-technical context of the present-day workplace. As opposed to other automation tools, GenAI poses an immediate threat to "knowledge work," a kind of activity distinguished by non-routine problem-solving, divergent thinking, and production of intellectual capital. In the past, this type of work was believed to be immune to automation, reserved solely for humans due to its high level of complexity and unique characteristics. Yet, recent developments have led to the emergence of GenAI systems capable of synthesizing extensive data sets, writing sophisticated text, producing code, and simulating human thought processes. Hence, a number of research papers has been published to explore the impact of these technologies on the productivity, organization, and nature of professional labor. The extant academic literature presents two diametrically different views on the effects of GenAI on knowledge work: while some researchers suggest that these technologies have democratizing potential and may help to empower knowledge workers, others emphasize that these innovations can displace human labor completely. On one hand, the existing scholarly work describes the positive outcomes of the introduction of GenAI in knowledge-intensive industries: it serves as a "second brain" of a knowledgeable worker, freeing him from mundane and time-consuming activities and allowing him to engage in strategic and innovative work. The analysis of empirical evidence from software engineering, consulting, and legal industries has confirmed the efficiency and productivity of these technologies among novice employees. On the other hand, GenAI can lead to deskilling and loss of professional competencies. Scholars are worried about the "black box" of GenAI, including issues of algorithmic bias, factual hallucinations, and loss of critical thinking abilities. In the paper at hand, the author presents the overview of the contemporary literature dedicated to the multifaceted influence of generative artificial intelligence on knowledge work. First, the ways in which GenAI disrupts traditional workflow will be described. Then, the findings of studies related to productivity increases and labor processes will be discussed. Finally, the ethical, cognitive, and organizational challenges connected to using GenAI in professional settings will be addressed.

**Introduction** AI technologies are undergoing a paradigm shift with the sudden growth of generative artificial intelligence (GenAI), fuelled by the extensive release of large language models (LLMs) such as GPT-4. GenAI isn't like the past waves of automation that focused mostly on automating routine low-coding and manual tasks, it is in and of itself "knowledge work. Long considered a uniquely human arena of work not subject to technological change, knowledge work is characterized by (among other things) non-routine problem solving, divergent thinking, and formation of intellectual capital. But the era of such constraints has been overrun by recent breakthroughs in GenAI's capacity to fuse massive amounts of data, craft intricate prose, develop operational code and emulate human reasoning. As a result, an expanding academic literature has been developed to explore how these technologies are changing the nature of professional work and the productivity, structure and cognitive requirements for professional workers. There exist competing discourses about how GenAI is affecting knowledge work: as empowerment and displacement. The narrative surrounding GenAI is promoting it as a “second brain” or “co-pilot,” relieving high-value, strategic, creative duties from highly skilled professionals and streamlining administrative tasks. Tasks produced faster and of a higher quality, with the improvement particularly apparent in less-expert users, implying a 'democratizing effect' for certain fields of work—such as software engineering, consulting services, and legal work—show in important empirical studies. On the other hand, the dangers of technological deskilling, cognitive atrophy and the loss of professional autonomy are pointed out. Scholars have voiced concerns regarding the "black-box" nature of GenAI, bringing potential for algorithmic bias, factual hallucinations, and reduced critical thinking skills when over-depending on AI-generated outputs. This literature review aims to capture a synthesis of the current research on the multifaceted nature of the impact of generative AI on knowledge work. First, it outlines how GenAI threatens to alter the dynamics of traditional processes, followed by a mapping of the empirical evidence on productivity and of the dynamics of labor. It is important to note that this section tackles the new ethical, cognitive and organizational issues of incorporating GenAI in professional practice. This review seeks to reconcile these conflicting views, offering a holistic perspective on the value, identity, and future of human knowledge and expertise in the ever-evolving landscape of GenAI.

Introduction The ultra-accelerated rise of Generative AI (GenAI)—spurred by the large-scale public launch LLMs, such as GPT-4—ushers in a paradigm shift withinour socio technical fabric across multiple layers that today define values and significant dynamics for work. Generation AI intersects with "knowledge work," unlike prior waves of automation that have largely been limited to low-complex manual and cognitive tasks. Knowledge work, with its signal feature of non-routine problem solving requiring divergent thinking and the generation of intellectual capital, was believed to be a uniquely human activity that insulated it from technological displacement. Yet these historical partitions have been shattered by recent developments in GenAI’s capability to absorb huge datasets, write complex prose, compose functional code and imitate human reasoning. Theretofore, there has been a surge of academic research delineating how these technologies and their variant implementations are transforming the productivity, composition, and cognitive requirements of professional labor ↗. The current research landscape for GenAI effects on knowledge work is a tale of two narratives: One centered around empowerment and the other focused upon displacement. Optimistic academic postulates GenAI as cognitive collaborator — "second brain" or "co-pilot," automating administrative drudgery and enabling top-tier professionals to orient on strategic, creative, and high-value tasks. Evidence from software engineering, consulting and legal services shows large improvements in task speed and quality, especially among less experienced workers — suggesting a levelling effect that will shrink skill gaps. In contrast, critical views emphasise the dangers of deskilling technologies, cognitive atrophy and loss of autonomy. Researchers caution that its "black-box" nature is conducive to algorithmic bias, factual hallucinations and loss of critical thinking skills due to over-reliance by professionals on the automated output. This rapid evidence assessment reviews contemporary studies that investigate the multi-dimensional effects of generative AI on knowledge work. It starts with outlining the channels through which GenAI disrupts conventional workflows and then follows by contextualizing what we have learned empirically about productivity growth and labor market dynamics. It also scans for evolving ethical, cognitive and organizational challenges associated with the emergence of GenAI into professional practices. By synthesizing these divergent views, this review also attempts to develop an integrated perspective on how GenAI is not simply automating work but fundamentally reshaping the value proposition of human expertise and its very nature.

The unprecedented explosion of generative artificial intelligence (GenAI), catalyzed by the public release of sophisticated large language models (LLMs) such as GPT-4, has ushered in a new era of technological transformation. While earlier waves of automation predominantly impacted routine, manual, and lower-order cognitive work, GenAI encroaches directly into the realm of "knowledge work," historically conceived as non-routine problem solving, creativity, and intellectual capital generation that was less susceptible to automation. The capacity of current GenAI systems to aggregate and synthesize information at scale, generate complex prose, draft functional code, and simulate human reasoning has shattered these perceived distinctions. Accordingly, an increasing body of academic literature has begun to investigate how this new wave of AI affects the productivity, organization, and cognitive intensity of professional occupations. Research on the effects of GenAI on knowledge work has coalesced around two diverging narratives: empowerment versus displacement. On the one hand, scholars tout GenAI as a cognitive partner, a "second brain" or "co-pilot," that can free skilled professionals from tedious administrative tasks to focus on more creative, strategic, and high-value endeavors. The growing body of empirical work, with studies in areas such as software engineering, legal services, and management consulting, provides evidence of increased task speed and improved quality of output, particularly among less-skilled workers, pointing toward a democratizing potential to narrow skills gaps. In contrast, on the other hand, there are dire warnings regarding technological deskilling, cognitive atrophy, the loss of professional judgment and autonomy, and the risks associated with relying on a "black-box" technology prone to error (e.g., hallucinations, algorithmic bias) which could lead to an overreliance on automated outputs and a diminishment of critical thinking skills. The following literature review seeks to integrate recent research that examines the multiple facets of GenAI’s impact on knowledge work. It starts by delineating how the current wave of GenAI is likely to disrupt traditional work processes, then reviews empirical evidence pertaining to the effects on productivity and employment, and finally discusses emerging ethical, cognitive, and organizational challenges associated with integrating this technology into professional practice. Ultimately, this literature review attempts to synthesize these conflicting perspectives in order to provide an organizing framework for understanding not just how GenAI might change how we work, but how it might ultimately change the very definition of valuable human expertise.

**Introduction** The rapid rise of generative artificial intelligence (GenAI), driven by the widespread release of large language models (LLMs) like GPT-4, marks a significant change in the modern workplace. Unlike earlier waves of automation that mainly focused on simple, manual, and lower-level cognitive tasks, GenAI directly affects "knowledge work." This type of work involves solving non-routine problems, thinking in new ways, and creating intellectual capital. It was once seen as a uniquely human field, protected from being replaced by technology. However, recent advances in GenAI’s ability to process large amounts of data, write complex text, generate code, and mimic human thinking have changed that view. As a result, there is a growing amount of academic research examining how these technologies are changing the productivity, structure, and cognitive demands of professional roles. Current studies on GenAI’s effect on knowledge work have divided views on whether it empowers or displaces workers. Supporters view GenAI as a cognitive partner, a "second brain" or "co-pilot" that takes care of tedious tasks. This frees skilled professionals to focus on more strategic, creative, and valuable work. Research in fields like software engineering, consulting, and legal services shows marked improvements in task speed and quality, especially among less experienced workers. This suggests that GenAI helps close skill gaps. On the other hand, critics warn about the risks of technological deskilling, loss of cognitive abilities, and diminished professional independence. Scholars are concerned about the "black-box" nature of GenAI, which brings risks of biased algorithms, misleading information, and a decline in critical thinking skills when professionals depend too much on automated results. This literature review brings together current research on the varied effects of generative AI on knowledge work. It starts by outlining how GenAI disrupts traditional workflows. It then reviews the evidence on productivity improvements and changes in labor dynamics. Finally, it looks at the ethical, cognitive, and organizational challenges that come with using GenAI in professional settings. By bringing together these different views, this review seeks to provide a thorough understanding of how GenAI is not just automating tasks but is also fundamentally changing the value, identity, and future of human expertise.

Let’s be honest—generative AI (GenAI) has turned the modern workplace on its head faster than anyone expected. Since large language models like GPT-4 arrived, we’ve seen way more than just another wave of automation. This time, it’s not about replacing factory workers or speeding up spreadsheet jockeys. GenAI is coming straight for what’s always felt like the most “human” work: solving tricky problems, thinking creatively, and building ideas out of nothing. For years, people figured these jobs were safe, tucked away from technology’s reach. Now, GenAI’s got other plans. It pulls information from huge pools of data, drafts complex writing, cranks out real code, and even mimics human logic. Suddenly, all those old boundaries between human and machine look awfully flimsy. No surprise, then, that scholars have jumped in with both feet, publishing study after study about what this all means for people in knowledge-based jobs. You can’t miss the divide in how folks are talking about it. Some researchers are excited. They see GenAI as a sort of digital partner—a “second brain” that takes care of mindless paperwork, letting experts zero in on work that actually matters. It’s pretty compelling: studies in everything from software engineering to law show that GenAI helps people work faster and, especially for beginners, lifts the quality of what they produce. This could shrink skill gaps and let more people tackle tough jobs. But not everyone’s buying the optimism. Critics worry about what professionals lose when a machine starts handling the heavy thinking. If you lean too hard on GenAI, do your skills atrophy? Does your judgment start to slip? There’s also that “black box” problem—you get answers, but good luck understanding how the AI got there. This opens the door to biased, misleading, or flat-out wrong outputs, and makes it harder for humans to push back or think critically about what’s really going on. This review pulls together what we know so far about how GenAI is upending knowledge work. First, it breaks down how exactly these AI tools are changing work routines. Then, it looks at the evidence on productivity and what happens to labor when AI moves in. Last, it digs into the deeper questions: What happens to professional identity and decision-making? What are the new ethical headaches? How do teams and organizations adapt? If there’s one thing that becomes clear, it’s this—GenAI isn’t just taking over tasks. It’s forcing us to rethink what it means to be an expert, what’s valuable in human work, and how we define ourselves in an AI-saturated world.