Generative AI has prompted a variety of responses from users since Chat-GPT launched. Previously the remit of experts, LLMs are now the focus of a wider public debate on the future of work and the value of human labor more broadly. Anxieties around revolutionary new technologies are not new – when steam trains began as passenger transport, concerns were raised about the high speed potentially rendering passengers mad. 

While the debate about the role of AI in our professional, educational and personal lives rages on, we do not currently know how pervasive and commercially viable generative AI will become. Will it become a semi-public good, with most people being able to access AI tools either for free or in relatively affordable SaaS (software-as-a-service) packages, or, after its initial introduction, will the highest-performing AI models be so costly as to remain the preserve of large well-funded companies that deploy it with little oversight or transparency? At present, it looks as if AI will become ubiquitous. 

But an even more fundamental question is whether AI will replace human labor and decisions or play a largely assistive role, facilitating human decision-making in the face of information overload. Both options stand to create substantial economic gains, and a recent Goldman Sachs report predicted that generative AI could raise annual US labor productivity growth by just under 1½pp over ten years following widespread adoption. Such expectations depend on the difficulty that AI can perform and the number of ultimately automated jobs, suggesting that the most optimistic of these scenarios assume that a wide range of jobs currently performed by humans will be rendered obsolete. 

Generative AI in Human Resource Management (HRM)

Contemporary strategic human resource management (SHRM) builds on human capital and resource-based view (RBV) notions that, given the right circumstances, employees (human resources) can be a source for sustained competitive advantage. Digitalization and technological developments such as ChatGPT can have disruptive effects and even cause a Schumpeter shock comparable to the development of digital photography in contrast to mainstream photo film rolls in the 90s. Barney's (1991) classic RBV article describes disruptive technological developments and Schumpeter shocks as phenomena that breakthrough sustained competitive advantage positions of organizations.

Reflecting on the past 30 years of research, the resource-based view is one of the most dominant theories in SHRM (Paauwe, 2004). Looking at the implications of widely applied ChatGPT soon, we foresee a severe reduction of the possibilities to achieve a sustained competitive advantage

through SHRM. After all, considering the four conditions, as formulated by Barney (1991) they will lose their relevance:

• Value: ChatGPT is freely available for everyone, so there is no or limited possibility to generate and reap the rents as proprietary rights are lacking. The rents and the benefits are there for everybody who knows how to handle and apply the software. 
• Scarcity: Not anymore. This is not only the case for ChatGPT but increasingly for all kinds of open-source software.
• Imitation: This used to be the core of the RBV theory, especially for achieving a 'competitive advantage through people' (Pfeffer, 1994), and clarified by the conditions of 'causal ambiguity', 'path dependency' and 'social complexity'. That kind of protection of a competitive advantage will no longer be applicable. ChatGPT is available for everybody.
• Substitution: Again, this condition has lost its relevance. ChatGPT represents all opportunities to replace or substitute HRM practices and professional activities.

ChatGPT can be very helpful in improving cost-effectiveness within HR. Think of activities such as composing job descriptions, screening applications based on job requirements, preparing semi-structured interview questions, and developing training programs and -materials such as course outlines and onboarding instructions. Also, writing policy documents, such as those related to absenteeism, hybrid working, and improving employee engagement, can be 'outsourced' to ChatGPT without incurring additional costs and saving a lot of manpower hours and expensive rates from consultancy firms. This transformation could create a temporary competitive advantage for the first-moving organizations and individuals.

Significant implications 

Generative AI is the latest in a stream of developments in AI that have been characterized as reflecting a paradigm change and a distinctive stage of industrial development, sometimes known as Industry 4.0. It is distinctive because it shifts the source and potentially the control of knowledge from people to machines. This has implications for management, workers and society at large to the extent that there have been calls to pause development while these implications are more fully considered. 

The central feature of generative AI, reflected, for example, in ChatGPT and Bard, is the capacity to store limitless amounts of knowledge and to present it in a coherent, usable form. It can undertake a range of activities that previously required human input. Therefore, it offers an attractive proposition to the industry by promising the availability of analyzed information of quality in great quantity and at a much faster speed than humans can achieve.