In April 2025, AI researchers Daniel Kokotajlo, Scott Alexander, Thomas Larsen, Eli Lifland, and Romeo Dean published a highly controversial yet influential report called “AI 2027.”¹ In it, they provide a vivid narrative interpretation of what a prospective timeline for the development of superintelligent AI would look like, featuring bioweapon deals between the US and China and the eradication of world poverty. Readers can even decide how the story ends by choosing between the “slowdown” or “race” endings. According to the fast track scenario, it ends with humanity being wiped out by AI (and sooner rather than later). 

But before this happens, “Agent-5”, the report’s superintelligent AI created by a fictitious tech company called OpenBrain, becomes the world’s best employee. Capable of working 100 times faster than the average human, Agent-5 speaks through avatars and starts running the US Government. Protests over job losses pick up pace, and humans are pushed out of factories and offices. But because the new superintelligent AI is creating massive economic growth, with GDP soaring and tax revenues increasing at an unprecedented rate, the government can actually provide unemployed workers with a generous universal income. Over time, humans resign to the fact that they are obsolete, with only a few workers remaining in a handful of industries. 

While some have praised the report for its impressive forecasting and extensive research,² others, like leading AI critic Gary Marcus, claim it’s too far-fetched and dystopian.³ Arguments aside, the picture painted by the authors in AI 2027 illustrates the anxieties many humans currently experience around AI and the future of work. In workplaces across the globe, colleagues jokingly remark how “AI could easily do this job” or “ChatGPT is amazing, it’s going to kick me out of the office.” But in a world increasingly driven by AI, what really is the future of work? 

Regardless of whether the AI 2027 project is closer to fiction than fact, AI is set to dramatically transform the future of employment, altering job roles, required skills, and the essence of work itself. While automation may replace certain jobs, AI is also anticipated to generate new opportunities and enhance human abilities. For employees to succeed in an AI-driven landscape, upskilling and reskilling will be essential to adapt to these shifts. Some have argued that AI will bring about a revolution among white-collar workers akin to that which was witnessed from the introduction of the assembly line in blue-collar work.¹⁷

Artificial Intelligence: A field of computer science and engineering concerned with creating systems that can perform tasks requiring human-like intelligence, such as learning, reasoning, and problem-solving. It also refers to the capability of machines to simulate these cognitive processes through algorithms and data-driven models.

Turing Test: A test of a machine’s ability to exhibit intelligent behavior, proposed by Alan Turing in 1950. In the test, a human interacts with both a machine and another human through text-based conversation. If the human cannot reliably tell which is which, the machine is considered to demonstrate intelligence.

Logic Theorist: An early artificial intelligence program developed by Herbert Simon and Allen Newell in 1955. It was designed to mimic human problem-solving by proving mathematical theorems. 

Information Age: The period, beginning in the mid 20th century, which witnessed a rapid shift from traditional industry to an economy based on information technology. It is marked by the widespread use of computers, digital communication, and the internet, enabling unprecedented access to, creation of, and exchange of information.

Large Language Models (LLMs): A type of artificial intelligence model designed to understand, generate, and manipulate human language. These models are trained on vast amounts of text data and use deep learning techniques to recognize patterns, grammar, and meaning in language.

Watson for Oncology: An AI-powered platform developed by IBM, designed to assist oncologists in diagnosing and treating cancer. It uses machine learning and natural language processing to analyze vast amounts of medical literature, clinical trial data, and patient information to recommend personalized treatment options.

For centuries, humans have been finding ways to enhance their productivity and efficiency using external tools. In 1950, British mathematician Alan Turing asked the provocative question “Can machines think?” and introduced the Turing Test as a way of evaluating machine intelligence. The test proposed that if a human conversing with a machine through text could not reliably distinguish it from another human, the machine could be said to exhibit intelligence. Five years later, pioneering American computer scientists and psychologists Herbert Simon and Allen Newell built on Turing’s theoretical foundations to develop the world’s first artificial intelligence program called Logic Theorist.⁵ The pair wanted to prove that computers could perform tasks traditionally reserved for human intellect, such as reasoning through complex problems and proving mathematical theorems. The program was successfully able to prove 38 of the first 52 theorems in Principia Mathematica, a landmark work in logic and mathematics written by Alfred North Whitehead and Bertrand Russell in 1913.⁶

At the time, programs like Logic Theorist were extremely expensive and only used by a select handful of scientists. It would be decades before technologies like these became commonplace, and fears of AI taking over the world remained largely confined to the pages of science fiction. However, industries like manufacturing, clerical work, and telecommunications were already grappling with automation, robotics, and computerization—changes that provoked many of the same anxieties about job loss and technological disruption that AI provokes today. This period is known as the Information Age, or the Third Industrial Revolution, and was characterized by a rapid shift from the traditional industries that we set up during the previous Industrial Revolution to economies centered around information technology.³¹ 

Fast forward to the 21st century, and we’re now experiencing what some believe to be the Fourth Industrial Revolution,³⁰ thanks to recent breakthroughs in areas such as artificial intelligence and biotechnology. 

Computer programs and technology have truly been democratized—everyone has access to high-tech tools that can help them to create, collaborate, and innovate. From smartphones and open-source software to cloud computing and AI-powered platforms, individuals and businesses alike can leverage these technologies to solve problems, automate tasks, and bring their ideas to life, regardless of their technical expertise or resources. And that’s just the problem. 

Research suggests that by 2030, up to 30% of hours worked across the US economy could be automated.⁸ That’s 30% fewer human hours. Yet it’s not all doom and gloom. According to the World Economic Forum, while automation will inevitably displace jobs, new technologies could create even more.⁹ In fact, the rise of automation and AI is expected to lead to the creation of entirely new job categories that we can't yet fully envision, requiring new skills and expertise. As industries evolve, the workforce will need to adapt, with many employees transitioning into roles that focus on more complex and creative tasks that AI cannot easily replicate. This shift could lead to a more productive economy, although it will require significant investment in reskilling and education to ensure workers can thrive in this new landscape.

As these technologies become more accessible, they also introduce both new opportunities and challenges. We are now in an era of human-machine partnership that promises to reshape the modern workplace.⁷ The widespread use of AI and automation raises important questions about the future of work, including the displacement of traditional jobs, the need for reskilling, and the ethical implications of relying on machines for decision-making. 

Alan Turing

British mathematician and computer scientist, regarded as the father of modern computing and artificial intelligence. He developed the concept of the universal Turing machine, helped break the German Enigma code in World War II, and proposed the Turing Test to assess machine intelligence.

Herbert Simon 

American psychologist, economist, and computer scientist, known for his groundbreaking work in cognitive science, artificial intelligence, and decision theory. He won the Nobel Prize in Economics in 1978 for his research on decision-making in organizations. 

Allen Newell

American computer scientist and cognitive psychologist, famous for his contributions to artificial intelligence and cognitive psychology. Alongside Herbert Simon, Newell co-developed the Logic Theorist, the first AI program, and the General Problem Solver. 

While we’re already starting to see the effects of AI integration in the workplace, much of its future impact remains speculation.  While AI offers opportunities for innovation and productivity, its effects on employees depend on how the technology is implemented and the support provided during transitions. 

Creating new jobs

Despite concerns about losing jobs to automation, the integration of AI into the workplace is actually expected to create new jobs. In fact, research by McKinsey Global Institute finds no concrete evidence that AI will wipe out jobs—that’s a fear created by humans.⁸ Instead, the available jobs will start to change, which in turn will require us to study new areas and learn new skills.⁷ 

Researchers from MIT have defined three categories of new job roles: trainers, explainers, and sustainers.¹⁸ Trainer roles involve human workers teaching AI systems how to perform tasks and making sure these systems function effectively for the people who use them. This may also include organizing and managing data. Explainers will need both technical and communication skills to help users understand what the AI is doing and why, addressing the issue of AI being a “black box” (the idea that the inner workings of AI are difficult to explain to lay people). At last, sustainers will make sure AI systems stay fair, transparent, and aligned with their original purpose over time. These roles might also involve ongoing training to help workers collaborate better with AI.

Innovation

According to current predictions, AI-driven workplaces will relieve humans of mundane tasks such as data entry and routine administrative work, leaving room to focus on creativity and problem-solving. By processing vast amounts of information, AI can help us identify invisible opportunities that would have taken a human years to find.  This, experts argue, will lead to greater innovation. 

In the medical sector, AI is already helping to accelerate research and bring new therapies to the market more quickly.¹⁰ For designers and marketers, AI enables teams to mine data like customer feedback or market trends at speeds impossible for humans, allowing them to focus on developing new solutions and ground-breaking products²⁶ And in the education sector, AI is driving innovation by enabling adaptive learning platforms with content tailored to individual student needs, improving engagement and performance.²⁷ 

Productivity

AI can boost our productivity by automating tasks, optimizing processes, and helping with decision-making. In a large U.S. study from 2023, a group of researchers explored how the use of Large Language Models (LLMs) impacted performance on knowledge-intensive tasks.²⁸ The experiment, which involved 758 consultants from global firm Boston Consulting Group, examined three conditions: no AI access, GPT-4 AI access, and GPT-4 AI access with prompt engineering training. The results showed that consultants using AI were significantly more productive, completing 12.2% more tasks and finishing them 25.1% faster. Additionally, AI users produced higher-quality results, with output improving by over 40%. However, when tasks fell outside AI’s capabilities, performance decreased by 19%.

In another study, researcher Erik Brynjolfsson and colleagues examined  how a generative AI-based conversational assistant impacted the productivity of the employees in a call center.²⁹ They found that access to the tool increased productivity—which was measured by the number of issues resolved per hour—by 14%. This figure increased to 34% among novice and low-skilled workers, but didn’t have much impact on more experienced call center staff. Together, these studies suggest that AI has the potential to meaningfully enhance productivity, especially for knowledge-intensive and entry-level tasks.

While AI brings ample benefits to our work, there are still major challenges. AI has permeated our jobs and everyday lives at an unprecedented pace—it took 75 years for fixed phones to reach 100 million users globally, while ChatGPT achieved that in two months.²⁵ This breakneck development has left humans on the back foot in areas such as mental health, privacy, and inequality. 

Mental health

Is AI in the workplace good or bad for our mental health? This is one of the key areas currently being researched, and the evidence is mixed. Some studies suggest that AI adoption significantly increases job stress, leading to burnout, as employees struggle with the pressure of adapting to new technologies and the fear of job displacement.¹⁹ In contrast, other research has shown improvements in mental health due to AI’s ability to reduce the physical demands of certain jobs, allowing workers to focus more on cognitive tasks and reducing physical strain.²⁰ 

However, the overall impact on mental health largely depends on factors such as job type, AI implementation strategies, and the level of support provided to workers during digital transitions. A young, digital native is less likely to feel anxious when asked to use AI tools than a senior manager who started their career when computers were a luxury. Similarly, AI is likely to have a greater impact on a creative designer who takes pride in owning their work than on a data entry clerk, who can process more information efficiently with AI assistance. 

Privacy

Another debate regarding the use of AI in the workplace stems from concerns about the erosion of privacy. Many companies now use AI to track and monitor workplace performance, but this has made many employees feel uncomfortable and surveilled.²¹ Rather than helping bosses pick up on odd behavior or productivity issues, AI monitoring can lead to more stress and a breakdown in trust. 

There’s also the risk that employees using AI tools might accidentally share sensitive data online. This is particularly problematic if personal data is included in the data they’re inputting to AI tools. As a result, lots of companies have started reigning in their employees’ access to AI tools. In 2023, for example, Samsung banned the use of ChatGPT after they found a leak of sensitive code on the platform.²² Similarly, JPMorgan banned the sharing of sensitive information with the chatbot among their staff.²³ To overcome these challenges, companies are coming up with strict data privacy policies that take into account new ways of working with AI. 

Inequality

AI-driven productivity gains are primarily benefiting wealthy nations and major tech companies, leading to the rise of a few dominant global players. Experts predict that in advanced economies, about 60% of jobs could be impacted by AI in the future. However, in emerging markets and low-income countries, the figures are much lower—40% and 26%, respectively. This means that while developing economies may face fewer immediate job losses from AI integration, they also don’t have the same infrastructure, skilled workforces, or digital literacy to harness the benefits of AI as in more developed countries. This disparity means that over time, AI technology could perpetuate or even worsen inequality among nations: as the most prosperous countries get the lion’s share of the benefits of AI, developing nations are left behind.²⁴ 

At the societal level, AI may also exacerbate inequality within countries. Workers in routine or lower-skilled jobs are at greater risk of displacement, while those with advanced education and digital skills are better positioned to benefit from AI’s productivity gains.²⁵ This effect could widen wage gaps and create new divides between those with access to AI tools and training and those without. In this way, AI does not only risk reinforcing global inequality but could also deepen income and opportunity disparities within societies.

Can AI help in the fight against cancer?

In the medical sector, AI is already transforming the workplace by giving professionals powerful tools to enhance diagnostics, streamline administrative tasks, and improve patient care. But sometimes even the biggest players in the market experience failure when integrating AI into work. 

In 2015, global technology company IBM launched Watson for Oncology, a system designed to assist doctors in diagnosing and treating cancer through AI insights. It promised to streamline clinical decision-making, bridge knowledge gaps, and improve patient outcomes.¹⁴ And IBM had reason to be confident. Four years before, an earlier iteration of Watson had shocked the world when it won the popular television quiz show Jeopardy, beating the two all-time champions.¹² Based on this astounding performance, IBM decided to put Watson to better use, addressing one of the world’s biggest health challenges: cancer. 

However, by 2017—nearly three years after the supercomputer was sent out to hospitals across the globe—it was still struggling with the basic step of learning about different forms of cancer.¹³ In trials and pilots in countries such as India and China, Watson’s treatment recommendations were inconsistent with local clinical practices. Because the supercomputer was trained on US-centric guidelines, its recommendations weren’t compatible with differing treatment standards or drug availability.¹³ IBM even came under fire for inputting synthetic data based on personas of cancer patients, rather than relying on actual past medical records.¹⁵ In some cases, Watson provided unorthodox and unsafe treatment options. 

It became clear that Watson for Oncology wasn’t working for the doctors it was meant to be helping. Eventually, in 2022, the IBM Watson Health program was sold to an investment firm.¹⁴

Workplace safety

AI not only promises to transform what our work looks like; it’s also shaping the safety of our workplaces. By leveraging real-time data and advanced sensors, AI can help prevent accidents before they happen.¹¹ Smart wearables can track workers’ movements and alert them when their posture or actions could lead to injury, while AI systems monitor environmental conditions like air quality or machine malfunctions, flagging potential hazards instantly. Predictive analytics powered by AI can even forecast safety risks based on patterns in accident reports, equipment performance, and worker behavior, allowing businesses to take proactive steps.

Yet with these innovations comes concerns around ethics and data privacy. As AI systems collect vast amounts of personal and environmental data, there is a growing risk of misuse or unauthorized access. Workers’ movements, health metrics, and even psychological states could be tracked, raising questions about consent and surveillance. Additionally, there are concerns about bias in AI algorithms, potentially leading to unequal safety measures or discrimination. One of the challenges for the future, therefore, is balancing the benefits of AI-driven safety with the protection of employee rights and privacy.

Another challenge related to AI and workplace safety is over-reliance on automation. If health and safety officers delegate too much responsibility to computers, human oversight will be reduced, which could lead to greater risks. Furthermore, the International Labor Organization argues that algorithm-driven workloads and being continuously connected to computers and AI systems could contribute to stress, burnout, and mental health issues. In other words, too much AI could make workplaces less healthy and safe.¹⁶ 

How to keep work meaningful in the age of AI

Artificial intelligence is reshaping the workplace and the implications for employee engagement. In this article, Triumph Kerins, Dr. Sekoul Krastev, and Sarah Chudleigh discuss how AI can threaten workers’ sense of purpose by reducing autonomy and control over their tasks. Drawing on self-determination theory, they propose that to maintain meaningful work, organizations should design AI systems that support autonomy, competence, and relatedness. 

Is it truly mine? How to use AI without sacrificing a sense of ownership

Integrating AI into creative processes brings with it certain psychological challenges. In this article, Yael Mark explores the "AI Ghostwriter Effect," where users often feel disconnected from AI-generated content, leading to diminished ownership and authorship perceptions. She emphasizes that AI should be viewed as a collaborative tool that enhances human creativity, rather than a replacement, and advocates for design approaches that maintain users’ sense of agency and involvement in the creative process.

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