The rise of AI (Artificial Intelligence) has created disruptions in various fields, ranging from agriculture to zoology and its shadow is being seen in the arena of Ph.D. creating a question about whether it is losing its relevance before the quantum level power of AI, creating concern for the scholars, academicians and other researchers today as on one hand we have the researchers who state that the rigor and discipline involved in the doctoral training process assists the scholars in developing a deep domain level knowledge in their topic area, giving them the ability to navigate through ambiguity, which is a human trait that is hard to replicate. However, the critics state that the formal credentials are giving way to demonstrable skills, which are adaptable and agile and that AI is now capable to absorb, synthesize and create knowledge at a scale unlike anything seen before and now a claim that ‘AI can do in 3 days what a Ph.D. Scholar can do in 3 years’ is no longer ridiculous creating a concern for the future of specific topic domain research in today’s age of AI. Industry experts like Vinod Khosla has even stated that the traditional degrees are losing their value in today’s age of AI. Such a claim can be made as the trend is shifting towards Generative AI based personalization in learning where the AI portrays the role of an expert in any given field, advocating a need for curiosity and adaptability over formalized qualifications. In addition, this shift has been reflected in industry reports which are stating that the need for realtime abilities and continuos learning is on the rise as the speed in which businesses, technologies and domains are evolving, creating a demand for education and skills which will work with and gel with AI today. AI has its own place and the Ph.D. too has its own place, as the paradigm shifting changes today have created a new frontier of complexity and ambiguity where the role played by deep, original inquiry still remains crucial today.  

A major axis in the debate is the difference between a research intensive role and a practionner position based role as in pure research, in the domains of frontier AI, interdisciplinary research and foundational science, the Ph.D. retains its relevance as a credential and as a training ground and the top tier research labs in universities, research institutions and other developmental groups (in areas of new algorithms and safety frameworks) require qualified, doctoral level researchers due to the expertise brought by the researchers in the designing, execution and interpretation of complex experiments and in the contribution of novel knowledge through creation rather than synthesis through existing models. That said, the roads into the applied AI and machine/deep learning jobs have been diversified and the practionners are now entering the field without advanced degrees by building portfolios, self-learning through AI-enhanced resources, completing targeted boot camps, or accumulating domain-specific project experience. Content creators and career guides emphasize that one can build a compelling AI career in 2025 without a Ph.D. if they master relevant technical skills, craft real-world project portfolios, and demonstrate the ability to fine-tune and integrate large models—especially for applied engineering, data science, or product roles, creating a bifurcation where the Ph.D. will be seen as a strategic choice, rather than a universal gateway, necessary for pushing the edges of knowledge which is optional for researchers keen on making an impact in arenas where execution is crucial than invention.

AI has an influence on the credentials economy and its reach extends beyond hiring preferences and into the domain of the conduct and valoration of research as generative AI tools are speeding the pace of research from literature reviews, data analysis and hypothesis generation to completion of entire written drafts, completing what now takes 3 months of this preliminary background work into 3 days. Some commentators frame this as a “reboot” of the Ph.D.: the core intellectual challenges remain, but the workflow shifts from solitary, manual synthesis toward human–AI collaboration where the scholar curates, critiques, and guides AI-generated insights. This evolution invites rethinking what doctoral training should emphasize—transversal skills such as critical evaluation of AI outputs, multidisciplinary synthesis, ethical reasoning, and communication—over rote immersion in volume. 

Simultaneously, there are growing concerns about credential inflation, reproducibility, and whether the proliferation of AI-assisted “novelty” dilutes substantive contribution; the value of a Ph.D. increasingly hinges on distinguishing genuine insight from surface-level recombination of existing knowledge. Initiatives like the HIRES-Ph.D. framework seek to bridge academic production and industry relevance by cataloguing and cultivating high-impact transversal skills, suggesting that the Ph.D.’s future may depend less on the title and more on how its training is adapted to modern expectations.  Employers’ evolving valuation of credentials, in part accelerated by AI’s ability to lower the signal cost of skills demonstration, raises practical pressure on doctoral programs. Firms increasingly deploy skill-based hiring, assessing candidates through portfolios, project-based evaluations, and on-the-job simulation rather than relying solely on degrees as proxies for capability. Studies into green and AI-related jobs show a measurable shift toward emphasizing individual competencies and continuous upskilling over formal qualifications—compounded by rapid job evolution where roles reconfigure faster than traditional academic programs can adapt. At the same time, some of the supposed obsolescence is counterbalanced by market demand: newly minted Ph.D.’s in AI-relevant fields are still commanding high salaries, and top companies continue to aggressively recruit doctoral-level talent for deep research, reflecting a “tiered” labor market where Ph.D.’s occupy both elite innovation niches and face competition from more flexible, skills-first rivals in applied domains. Furthermore, macro-level talent policy (e.g., national AI talent strategies) continues to treat advanced training pipelines—including Ph.D. production—as strategic assets for maintaining technological leadership, though with increasing calls to diversify pathways and make doctoral training more interwoven with industry exposure. 

So where does that leave individuals considering a Ph.D. in the age of AI? The decision is less about obsolescence and more about alignment: what are the intended goals, and how might doctoral training serve them in a landscape where AI magnifies both reach and redundancy? For someone aiming to contribute fundamentally new theory, to lead deep R&D teams, or to shape policy and ethics in high-stakes AI deployment, a Ph.D. (especially if updated to include AI collaboration literacy and real-world problem engagement) remains a powerful accelerator. Conversely, for those seeking to rapidly enter applied fields, build start-ups, or pivot across domains, alternative credentials, micro-credentials, apprenticeships, and demonstrable project mastery—augmented by AI tools—offer leaner, faster routes with less opportunity cost. A pragmatic middle path is emerging: hybrid models such as industry-aligned doctoral fellowships, professional doctorates with embedded practice, and modular post-baccalaureate upskilling that borrow the depth of Ph.D. thinking while shrinking the time and rigidity traditionally associated with it. Ultimately, AI doesn’t so much make the Ph.D. obsolete as it reshapes its ecosystem—raising the bar for what counts as distinctive doctoral value while widening the array of credible complements and substitutes. 

The notion that the Ph.D. is categorically becoming obsolete in the age of AI is an overstatement; what is changing is its unexamined primacy and the assumptions about what it guarantees. AI accelerates knowledge work, democratizes access to some kinds of expertise, and elevates the visibility of alternative pathways, putting pressure on doctoral education to adapt or risk marginalization in particular submarkets. At the same time, AI creates new, deeper problems—ethical, interpretative, foundational—that still benefit from, and in some cases demand, the reflective rigor and methodological sophistication of trained researchers. The future of the Ph.D. lies not in defending a static credential but in reorienting it: integrating human-AI collaboration skills, emphasizing transferable and evaluative capacities, and offering flexible, outcome-aligned formats. For individuals and institutions alike, the question is no longer whether to pursue or offer Ph.D.’s, but how to make them relevant, resilient, and distinguishable in a world where intelligence—artificial and human—is increasingly entangled