Navigating the Ethical Dilemmas of AI in Academic Libraries

  Navigating the Ethical Dilemmas of AI in Academic Libraries

As artificial intelligence (AI) becomes increasingly integrated into academic libraries, the principles that have governed their operations for generations face new challenges. Advanced technologies bring many benefits, from more efficient cataloging and retrieval systems to personalized recommendations that match readers with the resources most relevant to their needs. However, these capabilities often require massive amounts of user data, sparking critical conversations about privacy, consent, and potential misuse. The tension between maximizing AI's benefits and honoring core library values requires thoughtful reflection and urgent and proactive measures to address ethical dilemmas and maintain user trust.

The Critical Role of Academic Libraries in Safeguarding Against AI-Generated Content

A Guide to Protecting Inclusive Access and Intellectual Diversity

Addressing AI Biases in Libraries: Protecting Inclusive Access and Intellectual Diversity

While transformative in many domains, artificial intelligence carries inherent risks, particularly in replicating and magnifying biases in its training data. Libraries, vital access points to diverse and equitable knowledge, face significant challenges ensuring that AI systems uphold these principles. For instance, a scholarly recommendation engine trained predominantly on publications from mainstream Western sources might systematically overlook valuable contributions from underrepresented regions or lesser-known scholars. Such exclusions can narrow the scope of intellectual inquiry, subtly marginalizing voices that already struggle for visibility in the global academic landscape.

How AI is Reshaping Library Staffing Requirements

  How AI is Reshaping Library Staffing Requirements

AI has emerged as a powerful force capable of transforming library work, fueling conversations about whether these changes will redefine staffing requirements. Over recent decades, libraries have already witnessed the gradual introduction of automated processes such as checking out materials, renewing loans, and managing basic metadata entries. These innovations grew out of a desire to streamline routine responsibilities and free staff members to concentrate on more specialized services. As artificial intelligence continues to evolve, there is concern that it could accelerate this automation trend to the point where fewer people are needed for front-line or behind-the-scenes roles. Circulation desks rely increasingly on self-service kiosks or advanced digital systems to track inventory. Back-office cataloging and classification could be partially handled by algorithms adapting to bibliographic record patterns. Some fear that this momentum toward automation might diminish the role of human workers who once managed or supervised these repetitive tasks.

Yet automation itself is not necessarily a new phenomenon. Libraries have long been centers of technological adaptation, even during earlier eras when computers were first introduced to manage records, maintain indexes, or support bibliographic databases. The shift toward digital processes was rarely instantaneous or uniform; it happened in stages, with specific segments of library operations becoming mechanized while others remained reliant on human intervention. The present moment differs in the sophistication and reach of AI, which can now analyze complex datasets, learn from user interactions, and draw context-sensitive conclusions in ways that traditional software could not. Where prior computer systems operated within narrow parameters defined by fixed code, contemporary AI can infer patterns, refine metadata recommendations, and respond dynamically to user behavior. This level of adaptive intelligence has the potential to transform much more than clerical tasks, prompting an ongoing conversation about the future role of library professionals.

In examining how AI might reshape staffing, it is essential to recognize how library work has already been segmented. Large institutions often subdivide responsibilities among distinct departments: technical services handle acquisitions and cataloging, public services manage outreach and reference, and administrative teams oversee higher-level planning. Automation does not always affect all these areas equally. Tasks that revolve around repetitive or predictable actions—like scanning barcodes, updating patron records, or matching titles to subject headings—lend themselves more readily to automated solutions. This pattern can create apprehension among staff whose roles have centered on these responsibilities, as they may fear redundancy. However, even in cases where automation becomes widespread, human oversight often remains essential for addressing anomalies, exceptions, and quality control. AI routines might decide how to classify a resource, but librarians still have a critical role in ensuring materials are accurately described and meet specific professional standards.

Another layer of complexity arises from the growing demand for advanced digital skills within the library profession. AI tools often require configurations, custom integrations, and continuous monitoring to perform effectively, so library staff increasingly need familiarity with data analytics, coding, or systems integration. These competencies sometimes extend to a deeper understanding of machine learning algorithms, statistical models, or software development principles. Although many library science programs now offer courses or specializations in digital librarianship, not everyone in the field has had the opportunity to acquire these more specialized skill sets. This reality can exacerbate anxieties about job security: staff who are unprepared to interact with AI systems may worry that their skill sets no longer align with the organization's future needs. Retraining existing personnel or hiring new staff with advanced technical expertise can prove costly for library administrations facing budget constraints. Funds that might otherwise be channeled into collections or community programming could be diverted toward recruitment, professional development, and licenses for emerging technologies.

Despite the challenges, the shift towards AI-driven changes in staffing requirements also brings distinct possibilities for innovation and growth. This shift is not purely a matter of eliminating positions but can include reimagining them. Librarians hold a wealth of domain knowledge that surpasses what any automated system can acquire from raw data. Years of hands-on experience with patrons, intimate familiarity with unique collections, and insight into how various disciplines use information all position librarians to serve as invaluable translators and navigators between technology and user needs. As automated tasks multiply, staff members can reorient themselves toward roles emphasizing interpretation, curation, and advanced research support. Librarians might become data consultants for faculty, helping them harness AI in their projects or lead cross-campus collaborations to integrate machine learning tools with library metadata. Rather than diminishing human contributions, AI could catalyze librarians to deepen their value by offering more personalized, high-level services.

This dynamic highlights an ongoing debate: whether AI stands to supplant human workers or complement their abilities. Many in the profession advocate a perspective that sees AI as an augmentative tool rather than a substitute for professional expertise. By adopting a growth mindset, librarians can envision AI automating only the most routine aspects of the job while they continue to drive creativity and decision-making. The human touch can remain irreplaceable when a librarian's work involves direct engagement with patrons—assisting with reference questions, guiding searches for specialized materials, or instructing on information literacy. AI tools might supply preliminary answers or route queries efficiently. Still, librarians are often better positioned to interpret nuanced academic inquiries, provide empathetic assistance, and connect users to broader institutional resources. Thus, roles may shift from repetitive, process-oriented tasks to more consultative or project-based responsibilities, offering librarians a chance to demonstrate their adaptability and depth of knowledge.

Underlying this shift, a fundamental challenge remains: ensuring that library professionals have the opportunity and support to acquire the necessary digital competencies. Retraining a workforce in coding, machine learning, or advanced database management requires institutional commitment. Workshops, certificate programs, or collaborations with computer science departments could become crucial. Some institutions might form partnerships with industry vendors to develop training sessions that cater to the specific AI tools in use. However, libraries lacking the financial means or administrative backing to invest in these initiatives risk falling behind in technology adoption and staff development. If the drive toward AI-based solutions is not matched by adequate professional development, a growing divide could emerge between well-resourced libraries and those operating with fewer resources. This divide might manifest in stark differences in the range and quality of each library's services, impacting user experiences and potentially exacerbating inequities within the profession.

Those who navigate the transition successfully may find that new roles and titles begin to proliferate. Positions like "AI Librarian," "Data Services Specialist," or "Digital Scholarship Coordinator" could join the ranks of conventional catalogers and reference librarians. Such roles include analyzing usage data to enhance recommendation engines, refining machine learning models with domain-specific metadata, or working closely with researchers on text and data mining projects. Libraries that embrace these possibilities could harness the momentum of AI to remain vibrant, forward-thinking hubs for scholarship and lifelong learning. In doing so, they might also better serve a user base increasingly accustomed to data-driven interactions. Having professional staff equipped to optimize these systems could result in more accurate search outcomes, robust research support, and innovative digital exhibits.

In this sense, AI can serve as both a test and a spark for transformation. While the risk of job elimination is real, especially for positions heavily tied to tasks prone to automation, librarians who adapt may find fresh ways to contribute, often at a higher level of engagement with scholarly and community activities. The selection, organization, and preservation processes that define library work can gain new facets as AI tools allow for deeper metadata analysis or more sophisticated user insights. Librarians might guide system developers in crafting user-centered tools that reflect the nuances of academic inquiry, ethical considerations around data, and respect for users' privacy. Such collaboration ensures that AI systems are used not merely to streamline workflows or reduce costs but to honor the library's core mission of empowering individuals through equitable access to information.

Still, not every library or librarian will have the same opportunities. The actual impact of AI on staffing will vary widely depending on institutional size, financial stability, and community expectations. An extensive research library with specialized collections and a robust budget may find it essential to attract or grow a cadre of highly skilled technologists. In contrast, a small public library might embrace more straightforward AI applications for circulation or basic analytics. In the latter setting, the staff might not need to master coding or advanced analytics; instead, they could focus on becoming adept users of easy-to-deploy AI solutions that free them up to do more community outreach or local programming. In either case, the infusion of AI compels a conversation about what librarianship truly values and aims to protect—local identity, user privacy, or accessibility.

Some commentators warn that automation must be introduced deliberately, with adequate safeguards for employees whose skill sets may be rendered less central. Phased approaches that gradually deploy AI functionalities give staff members time to adapt, cross-train, or explore new career trajectories within the library ecosystem. Leadership can play a pivotal role by articulating a clear vision of how roles will evolve. Transparency around AI adoption decisions can help mitigate anxiety: if employees understand why specific tasks are being automated, how the technology will be monitored, and what role they can play in guiding its development, they may feel more secure and empowered. In contrast, if AI tools are introduced hastily or with little communication, mistrust may mount, and valuable institutional knowledge could be lost if employees preemptively leave or disengage.

The experience of retraining or upskilling, while undeniably challenging, can also foster a renewed sense of purpose among library professionals. Many librarians entered the field due to a passion for connecting people with the information they need, and that passion can be reinvigorated when new technologies amplify what is possible. Tools that automatically generate metadata, for instance, can speed up or refine certain cataloging operations, enabling librarians to devote more time to curating collections in ways that reflect user interests and emerging scholarship. Staff might create more specialized guides or tutorials, collaborate with academic departments on digital humanities projects, or set up data literacy workshops that teach patrons how to interpret and interact with the AI-generated recommendations they encounter.

One significant element in this transformation is recognizing that technology alone cannot replace the creativity, empathy, and strategic thinking librarians bring to their work. AI might categorize resources based on patterns it detects, but it is limited by the data on which it has been trained. Librarians, by contrast, carry the capacity for holistic judgment, especially when confronted with unique or unanticipated user requests. Overdependence on AI could risk introducing biases—embedded in the training data—into library services, potentially skewing recommendations or failing to capture the complexity of specific research topics. Staff with a firm grounding in critical thinking can identify these blind spots, adapt the algorithms, or provide alternate ways to discover materials. This oversight role reinforces the notion that librarians and AI can work in tandem: the machine handles large-scale processing tasks while human professionals interpret, critique, and innovate around those results.

Many professionals see this juncture as an opportunity to redefine what it means to be a librarian in an era when information is more dynamic and abundant than ever. Librarians can take the lead in ensuring that AI development is guided by ethical, user-centered principles—something that demands direct input from individuals who understand how patrons seek, interpret, and rely on scholarly materials. Doing so helps shape the tools rather than merely reacting to them. This attitude forms the basis of a growth mindset: an awareness that while AI might disrupt existing practices, it can also energize the field by refocusing attention on the uniquely human aspects of library services—those that require insight, innovation, and a commitment to the public good.

Budgetary pressures remain an ever-present concern. Institutions that must carefully balance costs against benefits might hesitate before investing in advanced AI platforms or extensive professional development programs. Suppose library leaders consider the immediate financial outlay and the long-term implications of staff engagement, service quality, and user satisfaction. They may find that strategic AI adoption can pay off in that case. Cultivating a workforce comfortable with traditional librarianship and emerging technologies could yield a future-ready institution better prepared to respond to shifts in how knowledge is produced, shared, and preserved.

All these considerations suggest that the interplay between AI and library staffing is neither straightforward nor one-dimensional. While AI could reduce the need for specific front-line or back-office roles, it also opens new avenues for librarians to apply their expertise in a rapidly changing environment. The desire to maintain a sense of communal mission and uphold professional values like equitable access, reliable curation, and intellectual freedom serves as a powerful motivation for librarians to adapt proactively. If guided with care, AI adoption can breathe new life into the field, inspiring library professionals to redefine their roles and, in turn, reimagine the services that libraries provide. Rather than representing an existential threat, AI might become a driver of evolution, challenging libraries to integrate fresh capabilities while preserving the human dimensions that have always made them indispensable.

The Hidden Costs of Reliance on Proprietary AI for Libraries

  The Hidden Costs of Reliance on Proprietary AI for Libraries

Libraries worldwide have quickly identified the promise of artificial intelligence in improving user services and streamlining back-end operations. Automated cataloging tools, recommendation engines, and data-driven insights into how patrons engage with collections offer a chance at more profound, meaningful interactions with resources. However, while the possibilities are undeniably energizing, there are mounting concerns about institutions becoming passive recipients of technology produced and controlled by entities whose motivations and business models differ significantly from those of academic or public libraries. In particular, the role of large technology firms stands out as a crucial element in shaping not only the capabilities of AI but also the ideological and ethical frameworks within which these tools operate.

When a library becomes dependent on a proprietary tool from a major technology company, it not only subjects its entire process to the conditions set by that corporate entity but also risks a significant loss of agency. This loss of agency extends beyond practical risks to a more profound sense of diminished control. Historically, libraries have prided themselves on shaping their own services and governance policies, reflecting the needs of their specific communities and the broader profession's commitment to intellectual freedom and open access. However, when the tools come pre-packaged with restrictions and functionalities determined by external priorities, libraries may find that the core values they endeavor to uphold are at risk and urgently in need of protection.

One of the reasons technology corporations have become so influential in this sphere is their capacity to invest at a scale few academic or public institutions could hope to match. Tech giants have assembled enormous datasets culled from billions of users worldwide, which they can then feed into advanced machine-learning systems. Their research and development budgets dwarf those available to most libraries. As a result, the most cutting-edge AI typically arises from these environments, reflecting the computational muscle and expertise that are more readily found there. Consequently, libraries that want to remain at the technological forefront may find that developing in-house solutions requires a level of capital and staffing that is simply unrealistic. The fallback option—purchasing or licensing existing AI products—seems to offer a quicker, more efficient route to modernization.

However, this reliance on licensed solutions can bring unintended costs to a library's sense of identity and independence. The mission and character of a library can be intimately tied to its approach to collecting, curating, and granting access to information. Whether an institution emphasizes open-source software to align with a commitment to transparency or invests in user privacy mechanisms to maintain a trusted public space, these choices form a backdrop against which all technological decisions are evaluated. A library's use of externally developed AI is not merely a technical matter. However, a policy decision impacts how it handles sensitive user data, prioritizes specific collections over others, and interacts with broader educational objectives. If the software in question was primarily designed to serve the goals of commercial clients—often data monetization or advertising—its embedded assumptions and constraints might conflict with scholarly and community-oriented objectives.

Over time, one of the most troubling outcomes of this dynamic could be a homogenization of library experiences. Libraries relying on the same vendor ecosystem may offer nearly identical interfaces, recommendation algorithms, and data collection policies. This homogenization could overshadow the local flavor of how a particular institution organizes its collections, its special attention to specific research areas, or its unique stance on user privacy. This shift can challenge the identity of libraries that, for centuries, have thrived on local curation strategies designed to reflect the particular needs of their communities. If crucial decision-making capacity moves outside the institution's purview, a library's distinctive personality and mission might not just be at risk but face a significant loss.

Furthermore, there is a persistent worry about licensing agreements that typically accompany AI solutions from major vendors. These legal contracts can be intricate and restrictive, spelling out how data is processed, stored, and shared. In many instances, the vendor retains extensive control over the rules governing the flow of information. Though ostensibly the owner or steward of the user data, the library might not be free to retain complete sets of usage logs for long-term archival, research, or auditing purposes. The company providing the AI may demand that all usage data be erased after a specific timeframe or might explicitly forbid any retention that would allow a future re-analysis of how patrons interact with the system. This limitation severely hampers academic libraries interested in studying longitudinal data for improvements in service or for scholarly inquiries into user behavior. Such restrictions hinder the library's ability to improve its services and raise serious ethical concerns about user privacy and trust.

Conflicts can arise where data must be stored for legitimate institutional reasons—such as compliance with grant requirements, the need to evaluate the effectiveness of newly introduced services, or a desire to develop in-house analytics for planning. The library's interest in retaining user data for legitimate educational or operational goals might clash with the vendor's licensing terms or privacy frameworks. If a library cannot negotiate amendments to the contract, it may find itself forced to either forgo meaningful research opportunities or risk breaching the terms of its agreement. In this sense, licensing constraints could extend beyond the purely legal realm and strike at the heart of how libraries carry out their mission, from fostering scholarship to maintaining a transparent record of institutional history. These conflicts highlight the practical challenges of AI adoption and the potential for libraries to lose control over their operations and decision-making processes.

Much of the tension stems from the reality that major technology firms usually focus on global markets and universal product offerings. These platforms and AI services are designed to meet the most common commercial needs, such as personalized content delivery or large-scale data analytics for consumer behavior. On the other hand, libraries are mission-oriented institutions committed to providing equitable access to information, protecting intellectual freedom, and serving as guardians of scholarly records and public knowledge. The alignment between corporate objectives and library values may be partial at best and outright incompatible at worst. Situations can arise where corporate policies incentivize data collection and monetization in ways that run counter to librarians' ethical standards. If the technology is structured to gather detailed user profiles, libraries might find themselves unwittingly contributing to surveillance or data commodification. This potential for unwitting contribution to practices that counter library values underscores the ethical implications of AI adoption and the need for libraries to carefully consider the tools they use.

Academic libraries, in particular, face a precarious balancing act. They operate at the intersection of educational mandates, faculty research needs, and student resource access. They must respect various local, national, and international regulations regarding data protection, intellectual property rights, and academic freedom. When a major technology vendor imposes stringent rules about storing or repurposing data, the library must struggle between fulfilling institutional obligations and adhering to external constraints. For instance, an institution might need to keep detailed usage statistics to justify future budget proposals or measure the impact of specific collections on student success. If the vendor forbids the library from retaining these logs or repurposing them for institutional research, the library could be disadvantaged in internal policymaking and broader strategic planning.

Another dimension to this issue is the legal uncertainty surrounding AI applications. Data gathered from user interactions in library systems often touches on sensitive areas related to academic research, personal reading preferences, and even private communications. Many jurisdictions have strict rules about such data—from privacy acts to regulations governing the confidentiality of patron records. In the context of a proprietary AI system, the library might not be fully aware of how to process and store data, raising the prospect of unintentional non-compliance. Even if the library diligently negotiates specific terms with the vendor, the ever-changing regulatory landscape could mean new guidelines or laws contradicting the existing agreement. The cost and complexity of renegotiating or switching vendors might then become prohibitive.

The resulting ethical and legal landscapes can be remarkably delicate. Librarians have long been recognized as professionals safeguarding user privacy and upholding transparency in managing information. The perception that a library is simply turning over user data to a corporate entity—especially one with opaque data-sharing practices—could damage trust in the institution. It might also spur faculty, students, and the broader public to question whether the library fulfills its ethical obligations. Under these circumstances, librarians could find themselves uncomfortable attempting to explain or justify the intricacies of vendor agreements and AI functionalities that they have limited power to modify or understand. Transparency, a cornerstone of library operations, can become elusive when proprietary algorithms and confidentiality clauses stand between librarians and the complete picture of data handling practices.

To negotiate this terrain, libraries might be compelled to commit significant resources to legal consultations, contract negotiations, and ongoing compliance oversight. These tasks can strain budgets under pressure from shrinking public funding or rising subscription costs for electronic resources. At the same time, library staff with expertise in AI, data analysis, and contract law are often in short supply. Institutions could find that to maintain any sense of control or autonomy, they need to invest in specialized skill sets that enable them to interpret and, when possible, adopt these technological tools to align with institutional values. Such investments may be entirely justifiable but still heavily burden operational costs.

Some libraries might explore hybrid strategies that combine off-the-shelf AI solutions with open-source software components. In this scenario, the library licenses specific modules from a tech firm while retaining some core infrastructure in-house or relying on open-source frameworks that grant more freedom. This approach can help a library mitigate the risks of total dependence on any single vendor and enable deeper customization. However, such strategies demand coordination and technical expertise not all institutions can muster. There is also the risk that if the proprietary modules are central to the system's functionality, much of the library's day-to-day operations remain locked into vendor-defined processes and constraints.

These challenges highlight a future that could unfold in multiple ways. On the one hand, the widespread adoption of advanced AI within libraries might deliver powerful benefits to users: dynamic discovery tools, more profound insights into research patterns, and improved operational efficiency. If libraries can skillfully negotiate contracts and collaborate with vendors to embed library values in designing and deploying these tools, this future could align more with the institution's interests. On the other hand, if libraries take a more passive stance—simply importing vendor solutions without thoroughly assessing the long-term implications—they could gradually relinquish key aspects of their professional and ethical identities. Such a scenario would see libraries offering uniform services shaped by external commercial interests, with little local input into functionality or data practices.

Even the path of skillful negotiation has its obstacles. The reality is that many libraries, significantly smaller or less funded ones, lack leverage when dealing with corporate giants. Negotiating custom terms can be challenging, if not impossible when the other side is a global enterprise that has standard licensing terms it offers at scale. These companies might see limited benefit in tailoring their agreements to each small libralibrary'ss, especially if the returns from those relationships are negligible compared to their more lucrative business clients. Libraries that attempt to push back too firmly on data-sharing provisions or demand the right to audit AI algorithms may face inflated costs or be turned away in favor of other clients willing to accept the standard terms. The unfortunate result is that well-resourced institutions might secure more favorable agreements, while smaller libraries become locked into less ideal contracts or are priced out of advanced AI altogether.

In contexts where libraries operate as part of a larger consortium or network, collective bargaining can offer an avenue for more equitable outcomes. By pooling resources and presenting a unified front, libraries can negotiate improved licensing terms. This collective approach can help mitigate the power imbalance and ensure that smaller or underfunded institutions also gain access to essential AI tools without sacrificing their ethical or operational priorities. At the same time, organizing such consortiums around AI procurement adds complexity since members must reach a consensus on how data is managed, how costs are distributed, and how compliance will be monitored.

Strict licensing limitations on data use or storage remain a pressing issue that cuts to the heart of the library's role as a steward of knowledge. Libraries curate external content and generate records of how that content is accessed and utilized. This valuable dataset can fuel institutional research, historical archives, and improvements in service design. If an AI vendor disallows certain forms of data retention, the library might lose an essential lens into its operations. Tracking usage over the long term can reveal changing interests within a scholarly community, the success of new initiatives, or areas where access is lacking. Without the capacity to preserve such logs, libraries risk basing future decisions on incomplete or anecdotal evidence.

A further complication emerges with issues of ownership and intellectual property. AI systems often rely on continuous streams of user input to refine their models. Suppose the vendor claims ownership over any improvements to the AI from training on library data. In that case, the institution's contributions to refining the technology become a commodity controlled by the corporation. In principle, the vendor might provide updates or improved algorithms to all its clients. This could mean that the original library that helped refine the system sees only marginal benefits compared to the value the vendor gains. The question of who truly “owns" the" data and the appropriate boundaries of use can be notoriously fuzzy in such relationships. Libraries that sign agreements granting the vendor broad usage rights over anonymized or aggregated user data may later discover that the corporate entity has monetized the aggregated information well beyond the scope that the library initially envisioned.

All these concerns can converge in a way that forces academic libraries, in particular, into cautious decision-making. On the one hand, institutions want to provide students and faculty with state-of-the-art tools that support cutting-edge research and efficient access to scholarly materials. On the other hand, they must preserve their autonomy, guard user privacy, respect diverse ethical codes, and maintain a sense of local identity that fosters trust. Is it possible to integrate advanced AI systems created by large, profit-driven entities without incurring some loss of independent governance?

In many respects, libraries stand at a crossroads. Carefully chosen AI solutions could amplify their knowledge and services, but deploying such technology presents non-trivial challenges related to values and principles. For an institution to remain faithful to its unique character and mission, it must engage in robust and proactive discussions with vendors, clarifying and insisting upon certain operational boundaries. Libraries might explore or even develop open-source or community-driven AI frameworks that do not carry the same potential for heavy-handed contractual limitations. These alternative pathways, while potentially more resource-intensive and complex, could help maintain a healthier balance between innovation and independence.

Ultimately, the threat of becoming passive consumers of proprietary AI tools is not purely a technological issue; it reflects broader questions about how power and influence flow within the information ecosystem. Libraries have long asserted their standing as public goods, aligned with educational, cultural, and democratic values. Tech giants, in contrast, are typically beholden to shareholders, growth targets, and profit margins. Balancing these disparate aims requires vigilant oversight, deliberate negotiation, and willingness to consider less conventional or more labor-intensive solutions. It may also require that libraries band together—through professional associations, consortia, or cross-institutional initiatives—to speak with a collective voice capable of compelling more transparent and flexible licensing terms from AI vendors.

As the field continues to evolve, the crux is how libraries can harness AI without betraying the core ideals that have defined them for centuries. If they can achieve that equilibrium, artificial intelligence might serve as a powerful instrument for knowledge discovery, operational efficiency, and community engagement while retaining the library capacity for its own course. If they fail, a new chapter might emerge in which libraries find their autonomy overshadowed by external imperatives, losing something vital. The choices made now, and the negotiations undertaken with AI providers will shape the character and mission of libraries in ways that could endure for decades to come.