From General Intelligence to Sustainable Adaptation: A Critical Review of Large-Scale AI Empowering People’s Livelihood

Abstract

The advent of large-scale AI models (LAMs) marks a pivotal shift in technological innovation with profound societal implications. While demonstrating unprecedented potential to enhance human well-being by fostering efficiency and accessibility in critical domains like medicine, agriculture, and education, their rapid deployment presents a double-edged sword. This progress is accompanied by significant, often under-examined, sustainability costs, including large environmental footprints, the risk of exacerbating social inequities via algorithmic bias, and challenges to economic fairness. This paper provides a balanced and critical review of LAMs’ applications across five key livelihood domains, viewed through the lens of sustainability science. We systematically analyze the inherent trade-offs between their socio-economic benefits and their environmental and social costs. We conclude by arguing for a paradigm shift towards ‘Sustainable AI’ and provide actionable, multi-stakeholder recommendations for aligning artificial intelligence with the long-term goals of a more equitable, resilient, and environmentally responsible world.

1. Introduction

The rapid development of artificial intelligence (AI), particularly large-scale foundation models, has marked a transformative shift in the capabilities of intelligent systems [1,2,3,4,5]. Unlike traditional task-specific models, these large-scale AI models (LAMs) possess generalized reasoning and emergent cognitive abilities enabled by their massive parameterization and pretraining on vast datasets. The most prominent and widely recognized category of LAMs are large language models (LLMs), which focus primarily on textual data. However, the LAM paradigm also encompasses models capable of processing vision, audio, and multimodal inputs [6,7,8]. While these models demonstrate unprecedented potential to enhance human well-being by fostering efficiency and accessibility in critical domains like medicine, agriculture, and education, their rapid deployment presents a double-edged sword. This progress is accompanied by significant, often under-examined, sustainability costs, including large environmental footprints, the risk of exacerbating social inequities via algorithmic bias, and challenges to long-term economic fairness [9]. This paper aims to address a critical gap in the literature by providing a balanced, critical review of these dual impacts through the lens of sustainability science.

2. Background

2.1. The Evolution and Capabilities of LAMs

Since the introduction of the transformer architecture in 2017 [10], the development of LAMs has accelerated dramatically. This breakthrough paved the way for influential models in 2018, such as Google’s BERT [11] and OpenAI’s GPT-1 [12]. The scale and capability grew exponentially with GPT-2 (1.5 B parameters) in 2019 and the landmark GPT-3 (175 B parameters) in 2020, which popularized few-shot learning [13,14]. The public and industrial adoption of these models skyrocketed with the launch of the initial public version of ChatGPT in late 2022, built on the GPT-3.5 series [15]. This was quickly followed by the more powerful and multimodal GPT-4 in 2023 [16]. Concurrently, a diverse ecosystem of models emerged, including Google’s PaLM [17] and PaLM 2 series [18]. Importantly, this period also saw the rise of influential open-source or source-available models, such as Meta’s LLaMA [19] and BLOOM [20], which broadened access to large-scale architectures. Most recently, highly competitive open-weight models like DeepSeek-V3 have further diversified the landscape, offering cost-efficient alternatives to proprietary systems [21]. For clarity, these key milestones are summarized in Figure 1. These models, such as OpenAI’s GPT series [22] and Google’s Gemini [23], have demonstrated unprecedented generalization across diverse domains, from natural language processing (NLP) and code generation to vision-language tasks and robotic control [24].

2.2. Positioning This Review: A Taxonomy of the Literature and Research Gap

This shift from narrow AI to general AI signifies a technological breakthrough that redefines the human–machine interface, enabling machines to collaborate with humans at a higher semantic level [25,26]. This technological leap represents a fundamental rewiring of socio-economic systems, intelligent system design, and the broader software and hardware ecosystems [27,28]. Indeed, from a systems perspective, LAMs integrate perception, decision-making, and action within a unified architecture, reducing the need for complex pipelines. In recent years, LAMs have been rapidly adopted across critical domains, as follows:

• In the medical domain, LAMs power clinical decision support systems for disease diagnosis, treatment recommendation, and healthcare administration, improving accuracy and efficiency in patient care [29];
• In the agriculture domain, LAMs facilitate farming by analyzing remote sensing imagery, soil, and weather data to optimize crop yield prediction, disease detection, and personalized irrigation advice for farmers [30];
• In the education domain, personalized tutoring systems powered by LLMs offer adaptive learning pathways, automated content generation, and real-time student assessment, enabling scalable, individualized educational support [31];
• In the financial domain, LAMs enhance fraud detection, risk management, and algorithmic trading through advanced language-based analysis of transaction data and regulatory documents, thereby strengthening financial integrity and operational resilience [32];
• In the transportation domain, LAMs are dedicated to dynamic traffic prediction, route optimization, and autonomous vehicle decision-making, contributing to safer and more efficient mobility [33].

Given their pervasive impact, understanding the trajectory and significance of LAMs has become imperative for researchers, policymakers, and the public alike [34]. This paper aims to address this need by critically evaluating the dual-edged impact of LAMs on people’s livelihoods, systematically analyzing the trade-offs between their socio-economic benefits and sustainability costs.

While a rich body of literature has reviewed the rapid advancements of LAMs, existing works often approach the topic from distinct, circumscribed perspectives. Comprehensive technical surveys, such as the one by Patil and Gudivada [26], provide an excellent overview of the models’ underlying architectures and operational techniques but generally do not frame their analysis around the broader socio-environmental consequences of deployment. Concurrently, meta-level reviews, like that of Chang et al. [24], focus critically on how to evaluate LLM capabilities but not on the downstream sustainability implications of these capabilities in real-world livelihood domains. Other lines of research offer deep dives into specific sectors. For example, reviews by Vrdoljak et al. [29] and Qiu et al. [3] offer granular analyses of LAMs within health informatics. While invaluable, their domain-specific focus precludes a cross-sector synthesis needed to identify the systemic, overarching sustainability challenges inherent in the LAM paradigm itself. Broader reviews, such as Raza et al. [7], survey applications across multiple industries, yet they primarily described what these models can do rather than systematically applying a consistent analytical framework to evaluate the inherent trade-offs involved. Finally, high-level theoretical pieces, like Farrell et al. [6], provocatively reframed LAMs as socio-cultural technologies, offering a crucial conceptual lens but without conducting a granular, domain-by-domain analysis of the practical balance between their socio-economic benefits and sustainability costs. Consequently, a clear research gap exists: a comprehensive and critical review that systematically assesses LAMs’ applications across multiple key livelihood domains through the integrated lens of sustainability science. This paper is designed to fill this precise gap.

2.3. Methodological Rationale: Domain Selection

Moreover, the selection of these five specific livelihood domains, medicine, agriculture, education, finance, and transportation, is not arbitrary but is methodologically grounded in their centrality to human well-being and sustainable development. These domains serve as representative pillars that align closely with the United Nations’ Sustainable Development Goals (SDGs), a globally recognized framework for assessing societal progress. Specifically,

• Medicine directly corresponds to SDG 3 (Good Health and Well-being), the foundation of human welfare;
• Agriculture is fundamental to SDG 2 (Zero Hunger), representing the basis of human subsistence and our relationship with the natural environment;
• Education aligns with SDG 4 (Quality Education), which is crucial for individual development and social equity;
• Finance and Transportation act as critical enablers for the broader socio-economic system, underpinning SDG 8 (Decent Work and Economic Growth), SDG 9 (Industry, Innovation and Infrastructure), and SDG 11 (Sustainable Cities and Communities).

Together, these five sectors provide a comprehensive yet focused lens through which to analyze the dual impact of LAMs. They span the spectrum from fundamental human needs (health, food) to critical societal functions (education, economic activity, mobility), making them a robust and representative sample for a critical review of AI’s role in empowering people’s livelihood through the lens of sustainability.

Therefore, the objective of this paper is to provide a balanced and critical review of LAMs’ applications in five pivotal livelihood domains through the lens of sustainability science. It is important to clarify that this work is structured as a critical review, rather than an exhaustive systematic review following a strict protocol. The goal is not to catalogue every existing study but to move beyond a purely technical description to conduct a holistic analysis. Our literature selection strategy was thus purposive, focusing on seminal and representative studies that best illustrate both the transformative potential and the sustainability trade-offs central to our critique. Seminal works were identified based on their foundational impact and high citation counts, while representative studies were selected from high-impact journals, leading conferences, and recent influential preprints that showcase state-of-the-art applications or offer robust empirical evidence on the specific trade-offs discussed.

2.4. Paper Structure

To guide this inquiry, our review followed the conceptual framework laid out in Figure 2. This framework dictated a deliberate “bottom-up” argumentative structure. In Section 3, Section 4, Section 5, Section 6 and Section 7, we first examined domain-specific applications, treating key studies as illustrative cases to establish a grounded understanding. The central cross-comparison and synthesis are then presented in Section 8, where we critically analyze the overarching sustainability costs that cut across all domains. By synthesizing these cross-domain insights, this review aims to foster a deeper understanding of LAMs as powerful socio-technical systems and make the case for aligning their rapid evolution with the principles of environmental integrity, social justice, and economic resilience.

To present this balanced and critical analysis, the remainder of this paper is organized as follows. Section 3, Section 4, Section 5, Section 6 and Section 7 systematically review the applications and implications of LAMs across five pivotal livelihood domains: medicine, agriculture, education, finance, and transportation, respectively. In each section, we not only examined the transformative potential but also identified the domain-specific challenges and sustainability trade-offs. Building upon these domain-specific insights, we will then introduce Section 8, which, as previously mentioned, is dedicated to critically analyzing the overarching sustainability costs that cut across all applications of the LAM paradigm. Finally, Section 9 synthesizes the paper’s key findings. It moves beyond a simple summary to argue for an urgent paradigm shift towards ‘Sustainable AI’ and provides actionable, multi-stakeholder recommendations for aligning technological advancement with long-term human and planetary well-being.

3. LAM Applications in the Medical Domain

The integration of LAMs into the medical domain represents a transformative opportunity to advance the goals of SDG 3 (Good Health and Well-being), potentially enhancing the efficiency (economic sustainability) and equity (social sustainability) of healthcare systems globally. Social sustainability, in the context of this paper, refers to the design and deployment of AI systems in a manner that promotes equitable access to resources and opportunities, reduces health disparities, upholds social justice, and enhances the long-term well-being and resilience of all communities. This transformation is touching virtually every facet of medicine: clinical decision support systems now leverage LAMs to synthesize complex patient data and clinical guidelines in real-time [35]; advanced vision-language models are automating image analysis in radiology and pathology; generative approaches are accelerating target identification and lead optimization in drug discovery; and conversational platforms are extending specialist expertise to underserved regions via telemedicine, while also streamlining clinical coding to reduce the administrative burden on healthcare professionals. However, this profound potential is balanced by significant and deeply entrenched challenges surrounding data privacy, algorithmic fairness, model interpretability, and equitable access. This section therefore reviews these key domains through a sustainability lens, evaluating both their contributions and the critical trade-offs they entail.

3.1. Clinical Decision Support Systems (CDSSs)

LAMs are enhancing CDSSs by acting as powerful reasoning engines that can synthesize information from electronic health records (EHRs), clinical guidelines, and the latest medical literature in real-time. In particularly, domain-specific fine-tuning allows these systems to achieve high accuracy in complex tasks like differential diagnosis and the recommendation of guideline-based therapies [29]. By providing evidence-based suggestions at the point of care, these tools can help reduce diagnostic errors and standardize treatment quality [36]. More importantly, they have the potential to democratize specialist-level knowledge, offering crucial support to general practitioners or clinicians in under-resourced settings, thereby contributing to greater health equity. While studies highlight their technical capabilities, ensuring their safety and reliability within complex clinical contexts requires the development of rigorous, ongoing validation frameworks.

3.2. Medical Imaging and Radiology

Vision-language and multimodal LAMs are beginning to alleviate the immense workload on radiologists and pathologists. By integrating textual and visual data, these models can detect subtle radiographic features, such as early-stage lung nodules, with a diagnostic performance comparable to human experts [37]. Furthermore, AI algorithms, some of which are already FDA-cleared, are being used to pre-annotate imaging studies. This not only accelerates the diagnostic workflow but also helps mitigate clinician burnout, a critical issue for the social sustainability of the healthcare workforce [38]. By automating routine tasks, these tools free up expert clinicians to focus on more complex cases, potentially improving diagnostic throughput and patient access to care.

3.3. Drug Discovery and Personalized Medicine

LAMs are poised to improve the economic sustainability of pharmaceutical research by accelerating the traditionally slow and costly process of drug discovery. By employing generative and retrieval-augmented generation (RAG) techniques, LAMs can mine vast scientific literature, propose novel molecular scaffolds, and predict their bioactivity profiles [39]. This significantly shortens the initial phases of target identification and lead optimization. Furthermore, LAM-driven models are crucial for advancing personalized medicine. By integrating a patient’s unique genomic data with extensive pharmacological knowledge graphs, they can help identify optimal therapeutic strategies and expedite target prioritization in fields like oncology, offering new hope for treating rare and complex diseases [40].

3.4. Patient Interaction and Telemedicine

Conversational LAMs are enhancing healthcare accessibility and bridging geographical divides, a cornerstone of social sustainability in health [41]. Deployed as virtual assistants and chatbots, they improve patient education, provide medication reminders, and offer preliminary symptom-checking services, which has been shown to reduce clinic no-show rates by over 15% [29]. Moreover, telehealth platforms powered by empathetic LAMs are expanding access to mental health support, demonstrating engagement metrics comparable to human counselors [37]. From a broader sustainability perspective, telemedicine inherently reduces the carbon footprint associated with patient and clinician travel (environmental sustainability), while also lowering costs for both patients and providers (economic sustainability).

3.5. Sustainability Challenges and Future Directions in Medical AI

The integration of LAMs into medicine, a high-stakes domain, necessitates a cautious and critical approach. The literature reveals a recurring tension between the promise of enhanced efficiency and the risks of amplifying biases or eroding clinical trust. While some findings show remarkable potential, their robustness is often contested due to limitations in study design or data heterogeneity. The primary challenges are thus not merely technical but are deeply intertwined with these conflicting results and the broader ethical and social dimensions of sustainability, which will be presented in Section 8:

• Algorithmic Fairness and Clinical Trust: Perhaps the most significant challenge is the risk of LAMs perpetuating or even amplifying existing health disparities. Models trained on biased datasets, which may underrepresent certain demographic groups, can produce clinically valid but inequitable recommendations, undermining social sustainability. Furthermore, the “black box” nature of many models poses a barrier to clinical trust and accountability. As noted in the literature [42], a model’s inability to justify its reasoning can impede its adoption in scenarios where lives are at stake. Future work must prioritize the development of interpretable, fair, and transparent AI architectures.
• Data Governance and the Equity Divide: The effectiveness of LAMs is contingent on access to vast amounts of high-quality health data. This raises profound issues of data privacy, security, and ownership. Without robust governance frameworks, there is a risk that patient data could be exploited, or that privacy breaches could erode public trust [43]. Moreover, the high cost and technical expertise required to develop and deploy medical LAMs could widen the global health equity gap, creating a world where only affluent institutions and nations benefit from these advanced tools. It should be noted that federated learning (FL) offers a promising path for training models across institutions without centralizing sensitive data [44]. FL offers a promising path for training powerful AI models across multiple institutions without centralizing sensitive patient data. As comprehensively reviewed by Babar et al. [9], FL is a cornerstone of digital transformation in healthcare, designed to unlock the value of large, distributed datasets while upholding stringent privacy and security requirements [45]. By enabling collaborative model training directly on local hospital servers, FL can significantly mitigate the risks of data breaches and help address the “data governance” dilemma. However, the widespread adoption of FL is not without its own set of challenges, particularly the issue of data heterogeneity. In real-world medical scenarios, data distributions vary significantly across different hospitals due to diverse patient demographics, imaging equipment, and clinical protocols. In a pertinent empirical study, Babar et al. [46] demonstrated that the performance of standard FL algorithms drops significantly when trained on non-identically and independently distributed (non-IID) medical imaging data compared to idealized homogenous data. This performance degradation underscores that while FL provides a powerful framework for privacy, future research must focus on developing more robust algorithms that can effectively handle the data heterogeneity inherent in the healthcare ecosystem. Overcoming this hurdle is key to ensuring that the benefits of large-scale collaborative AI models are realized equitably and do not inadvertently penalize institutions with unique or smaller datasets.
• Economic and Implementation Barriers: While LAMs can improve efficiency, their implementation requires significant upfront investment in computational infrastructure, data management systems, and workforce training. For many healthcare systems, especially those in low-resource settings, the total cost of ownership is prohibitively high. This creates an economic sustainability challenge, limiting widespread adoption. Therefore, future research should also focus on creating lightweight, cost-effective, and easily integrable models that can function effectively within existing clinical workflows without demanding a complete infrastructural overhaul. The ultimate goal must be the development of hybrid human–AI collaborative systems that augment, rather than replace, human expertise.

4. LAM Applications in the Agriculture Domain

The integration of LAMs into agriculture presents a pivotal opportunity to address some of the most pressing global challenges, including food security, resource depletion, and climate change. By enabling data-driven decision support, precision resource management, and automation, LAMs offer a pathway toward more sustainable and resilient agricultural systems [47]. However, this technological transformation is not without its own complexities and potential pitfalls. This section reviews major application areas through a sustainability lens, examining both the profound benefits and the inherent trade-offs in using LAMs to reshape the future of farming.

4.1. Precision Farming and Resource Optimization

Precision agriculture, enhanced by LAMs, aims to optimize farming inputs on a granular level, directly contributing to environmental sustainability [48]. By integrating vast, multimodal data streams, including satellite imagery, weather forecasts, soil-moisture sensor data, and crop genomics, LAMs can generate highly contextualized recommendations.

For instance, systems powered by models like GPT-4 can interpret 10-day weather and soil data to create dynamic sowing and irrigation schedules that include risk mitigation strategies, significantly improving water-use efficiency and yield potential [49]. Furthermore, AI-driven analytics, often combined with 5G-enabled IoT infrastructure, have demonstrated the capacity to reduce the over-application of fertilizers and water by up to 25 percent in field trials, according to industry reports (https://www.myjournalcourier.com/opinion/article/precision-agriculture-investment-food-security-20260610.php) (accessed on 10 September 2025). This reduction not only provides economic returns for farmers but also curtails nutrient runoff into waterways, a major source of environmental pollution.

4.2. Crop Disease and Pest Detection

Early and accurate detection of crop diseases and pests is crucial for protecting yields and reducing reliance on broad-spectrum chemical pesticides. LAMs are revolutionizing this field by combining advanced computer vision with complex reasoning capabilities.

For example, frameworks integrating deep learning image analysis with graph attention mechanisms have achieved over 92% accuracy in identifying fungal infections on coffee leaves, demonstrating the power of domain-adapted pretraining [50,51]. More advanced systems now integrate object detection models like YOLOv8 with retrieval-augmented generation (RAG) capabilities. One such study on coffee-sector diseases in Karnataka showed that this approach led to a 30% reduction in false negatives while also enabling the generation of context-aware treatment advice for farmers, thus empowering them with timely and actionable information [52]. This targeted approach supports integrated pest management (IPM) strategies, reducing chemical usage and promoting biodiversity.

4.3. Yield Prediction and Crop Modeling

Accurate yield forecasting is essential for stabilizing food supply chains, managing market risks, and informing national food security policies. Foundation models trained on extensive multimodal datasets (satellite imagery, weather histories, soil surveys) are setting a new standard for predictive accuracy. A recent review [50] indicated that transformer-based models, when applied to historical yield and climate data, show significant improvements in robustness over older LSTM baselines across major cereals.

Beyond forecasting, LAMs are accelerating the development of climate-resilient crops. For example, Fernandes et al. [53] developed a sophisticated genotype-environment-management model that uses LLM feature encoders to analyze genetic markers and environmental covariates. This model achieved yield prediction errors below 5% in maize trials, a result that can dramatically accelerate breeding cycles and help develop new crop varieties adapted to future climate scenarios.

4.4. Livestock Management

In the livestock sector, LAMs are being deployed to enhance productivity, improve animal welfare, and promote sustainable grazing practices [54]. Specifically, systems driven by vision-language and sensor technologies enable real-time, non-invasive monitoring of animal health and behavior. In particular, vision-AI algorithms can automatically detect early signs of lameness or changes in feeding patterns, triggering timely alerts for veterinary intervention and preventing herd-wide health issues [55].

Furthermore, innovative applications are tackling environmental challenges like overgrazing. Reporting on the autonomous ‘SwagBot’ autonomous herder in Australia, for instance, highlights how embedded AI can assess pasture health in real-time to optimally route cattle (https://www.reuters.com/technology/meet-swagbot-ai-powered-robot-cattle-herder-preventing-soil-degradation-2024-12-12/) (accessed on 10 September 2025). This prevents soil degradation and improves weight gain metrics, demonstrating a clear synergy between economic productivity and environmental stewardship. Moreover, reference [56] reported that multimodal systems combining audio, video, and other sensor data are increasingly seen as key to holistic herd management.

4.5. Sustainability Challenges and Future Directions in Agricultural AI

Despite the promising advancements, a closer look at the findings revealed inherent contradictions [57,58]. For instance, while precision agriculture applications promise resource efficiency, they introduce a new digital carbon footprint. Similarly, the drive for optimization often conflicts with the need for ecological resilience [59]. The robustness of these AI-driven systems in the face of unforeseen climate events remains a significant, under-evaluated concern. These cross-cutting challenges require careful consideration, and their systemic nature will be discussed further in Section 8:

• Environmental Trade-Offs and the Digital Carbon Footprint: While precision agriculture can reduce on-farm resource use, the digital infrastructure it relies on has its own substantial environmental footprint. The manufacturing of sensors, drones, and edge devices, coupled with the immense energy consumed by data centers for training and running complex LAMs, creates a new set of environmental costs [60,61]. A true sustainability assessment must adopt a lifecycle perspective, balancing on-farm efficiencies against the carbon cost of the underlying technology.
• Socio-Economic Inequities and the Digital Divide: The high capital and knowledge requirements for implementing advanced AI systems risk creating a pronounced digital divide [62]. Large, well-capitalized agribusinesses can readily adopt these technologies, potentially consolidating their market power, while smallholder farmers, who constitute the majority of the world’s agricultural producers, may be left behind due to costs, lack of infrastructure, or insufficient technical literacy. Without deliberate policies to ensure equitable access, AI could inadvertently exacerbate rural inequality. This growing disparity is a critical sustainability issue because it undermines the social resilience of rural communities and threatens the long-term economic viability and diversity of the global food system, which depends on the participation of smallholder farmers.
• Data Governance, Farmer Autonomy, and Trust: The collection of vast amounts of farm-level data raises critical questions of ownership, privacy, and power. Who controls the data generated on a farm, and how is it used? There is a risk that this data could be exploited by large corporations, locking farmers into specific technology platforms or using their data to influence market prices. Building trust and ensuring farmer autonomy through transparent and fair data governance frameworks is therefore essential for the social sustainability and long-term adoption of these technologies.
• Resilience vs. Over-Optimization: LAMs are trained to optimize for specific outcomes based on historical data. This can lead to highly efficient but potentially brittle systems that are vulnerable to “black swan” events, unprecedented climate extremes, novel pests, or geopolitical shocks not present in the training data. A key future direction is to integrate causal reasoning and ecological principles into LAMs to foster genuine resilience rather than just narrow optimization, ensuring agricultural systems can adapt to an increasingly uncertain future.

5. LAM Applications in the Education Domain

Achieving inclusive and equitable quality education for all (UN SDG 4) is a cornerstone of sustainable development. LAMs are emerging as a powerful, yet controversial, force in this domain. They promise to revolutionize teaching and learning by enabling personalized, adaptive, and scalable solutions to address long-standing educational challenges [63]. This section systematically reviews these transformative applications, focusing on their potential to advance social sustainability through greater educational equity, while also critically examining the profound challenges that threaten this vision.

5.1. Personalized Learning Paths and Adaptive Tutoring Systems

One of the most significant promises of LAMs is their ability to dismantle the “one-size-fits-all” model of education that often fails to cater to diverse learning paces and styles. By analyzing behavioral data, cognitive patterns, and performance metrics, LAMs can tailor educational experiences to individual learners. Frameworks like the Large Language Model and Domain-Specific Model Collaboration (LDMC) [64] showcased this potential by integrating domain knowledge to dynamically adjust learning strategies and generate customized content. Similarly, Neuro-Symbolic AI Agents (NEOLAF) [65] combine neural networks with symbolic reasoning to provide real-time, targeted feedback, enhancing problem-solving efficiency. By empowering students with self-paced, context-aware learning journeys, these systems contribute directly to educational equity, offering the potential to close achievement gaps between students from different socio-economic backgrounds.

5.2. Intelligent Content Generation and Curriculum Optimization

LAMs are significantly streamlining the creation of high-quality educational materials, a task that has historically been a major burden on educators. They can automate the generation of lesson plans, assessments, and multilingual resources with remarkable proficiency. For example, studies have shown that models like ChatGPT-4o can generate medical education materials for children with high accuracy and appropriate reading levels [66]. This automation is a critical contribution to the social sustainability of the teaching profession. By leveraging tools like the AI teaching assistant to synthesize courseware and exercises, teachers can reduce their preparation time, allowing them to focus on higher-value activities such as mentoring, fostering critical thinking, and providing socio-emotional support [67]. This not only helps mitigate teacher burnout but also elevates the role of the educator from a content dispenser to a facilitator of deep learning.

5.3. Multimodal Interaction and Immersive Learning

By integrating vision, speech, and text modalities, LAMs facilitate more natural and engaging human–computer interactions, making learning more inclusive. Virtual teaching assistants can simulate scenario-based dialogues for language learning, while interactive avatars can guide children through complex concepts using a combination of visual aids and conversation [65,68]. These innovations are particularly beneficial for younger learners, non-native speakers, and students with diverse learning needs, such as those on the autism spectrum, who may benefit from alternative modes of interaction [66]. This multimodal approach makes abstract concepts more tangible and learning environments more welcoming for everyone.

5.4. Bridging Educational Gaps and Supporting Special Needs

A key measure of a sustainable education system is its ability to reach the most vulnerable. LAMs are helping to democratize access to quality education by addressing resource disparities. For students with disabilities, these models can provide real-time communication aids, such as converting speech to text for the hearing-impaired or simplifying complex texts for neurodiverse learners [69]. In under-resourced or remote schools, localized LAMs can offer multilingual support and culturally adapted curricula, reducing the dependency on a limited number of human instructors [70]. Critically, innovations like FrugalGPT, which employ cost-aware caching strategies, demonstrate a pathway to economic sustainability in EdTech, making AI-driven tutoring accessible even in low-bandwidth and low-budget environments [71,72].

While the potential for personalized tutoring is significant, it is crucial to maintain a critical perspective on the current capabilities and limitations of LAMs in educational settings, particularly in complex, skill-based domains like competitive programming. Recent empirical studies challenge the narrative that AI consistently outperforms humans. For instance, a comprehensive study by Koubaa et al. (2023) [73] evaluated ChatGPT’s performance against human programmers in the IEEEXtreme programming competition. Their findings revealed that while ChatGPT could solve simpler problems, its performance deteriorated sharply with increasing complexity, scoring on average 3.9 to 5.8 times lower than human competitors. This underscores a critical limitation: current LAMs still struggle with the high-level reasoning, algorithmic optimization, and creative problem-solving required in advanced competitive environments.

Furthermore, students themselves appear to adopt a nuanced, hybrid approach when choosing between AI tools and human-curated knowledge platforms. A study by Garcia et al. [74] explored student preferences between ChatGPT and Stack Overflow for resolving programming queries. The research indicated that while students value ChatGPT for its speed and interactivity, they continue to rely on human-curated platforms like Stack Overflow for more reliable and in-depth solutions to complex problems. These findings suggest that the most effective and sustainable integration of AI in education may not be a full replacement of traditional resources, but rather a symbiotic “human–AI partnership.” In this model, LAMs can act as instant-feedback tutors for foundational knowledge, while human expertise and collaborative platforms remain indispensable for mastering complex skills and fostering deep, critical thinking.

5.5. Sustainability Challenges and Future Directions in Educational AI

The integration of LAMs into education presents a series of profound dilemmas where findings often diverge [75]. For example, while some studies praise LAMs for personalizing learning, others (e.g., Koubaa et al. [73]) provide robust evidence that their performance on complex reasoning tasks is still far from the human level, highlighting a conflict between claimed capability and actual robustness. These tensions between efficiency and deep learning, personalization and bias, are central to the sustainability debate in educational AI and form a key part of our synthesis in Section 8.

• The Equity Paradox: Personalization vs. Bias: While LAMs promise to foster equity through personalization, they are trained on data from an often-inequitable world. There is a substantial risk that these models will absorb and amplify existing societal biases, creating personalized learning paths that steer students from marginalized groups toward less ambitious outcomes. Without rigorous fairness audits and bias mitigation strategies, the very tool designed to close equity gaps could end up cementing them.
• The Access Dilemma: Democratization vs. Cost: The vision of democratizing education is challenged by the high computational and financial costs of deploying state-of-the-art LAMs [71]. This creates a new digital divide, where only well-funded institutions and affluent families can afford the best AI tutors. This is both an economic sustainability issue and an environmental one, as the energy footprint of large-scale educational AI cannot be ignored [76]. The development of smaller, more efficient, and open-source models is crucial for ensuring that AI becomes a great equalizer, not a great divider.
• The Pedagogical Conundrum: Efficiency vs. Critical Thinking: An over-reliance on LAM-generated content and answers risks undermining the core mission of education: to cultivate critical thinking, creativity, and problem-solving skills. If students become passive consumers of AI-generated knowledge rather than active constructors of it, we risk a decline in cognitive resilience and intellectual curiosity [65]. The future of educational AI must focus on creating systems that act as Socratic partners or “intellectual sparring partners,” challenging students to think rather than simply providing them with answers.
• The Trust-Privacy Trade-Off: Effective personalization requires the collection of vast amounts of sensitive student data, creating an inherent tension with privacy and data protection. Building frameworks that ensure student data are used ethically, transparently, and solely for educational benefit is paramount. Without establishing this trust among students, parents, and educators, the social license for deploying these powerful technologies will quickly erode.

6. LAM Applications in the Financial Domain

LAMs are reshaping the financial sector by enhancing decision-making processes, automating workflows, and enabling sophisticated predictive analytics [77]. This chapter systematically reviews the transformative applications of LAMs in finance, focusing on their societal and economic impacts, supported by representative frameworks and case studies.

6.1. Intelligent Question Answering and Knowledge Management

LAMs are revolutionizing knowledge management by enabling the context-aware retrieval and synthesis of complex financial information. Specifically, traditional keyword-based systems are being replaced by AI-driven solutions that integrate external knowledge bases with LAMs via frameworks like Langchain. AI-driven solutions using RAG can dynamically access and interpret regulatory documents, market reports, and earnings calls [78]. For financial institutions, this translates into significant gains in operational efficiency for analysts and compliance teams. From a social sustainability perspective, this technology also holds the potential to democratize access to financial information, empowering retail investors with insights that were once the exclusive domain of large institutional firms.

6.2. Financial Forecasting and Risk Management

LAMs demonstrate powerful capabilities in predicting market trends and managing risks by analyzing vast, heterogeneous data sources, including news feeds, social media sentiment, and historical market data. Among them, hybrid architectures combining LAMs with time-series models (e.g., LSTM, transformer-based models) have been adopted to capture temporal dependencies in stock prices and macroeconomic indicators. For example, FinLLaMA [79], a financial-specific LAM, integrates text and time-series data to predict market anomalies and assess portfolio risks, outperforming traditional models in zero-shot scenarios. On the surface, this improves profitability; on a deeper level, it contributes to economic sustainability by enhancing systemic risk management. By automating stress testing and simulating economic scenarios under dynamic conditions, these tools can help regulators and institutions identify vulnerabilities before they escalate into systemic crises. Moreover, recent research highlights the move from coarse, document-level sentiment analysis to more granular, aspect-based approaches. A study by Jehnen et al. [80] introduced FinTextSim-LLM, a hybrid framework that uses a domain-specific transformer to analyze financial reports and extracts topic-specific sentiment. Their findings demonstrated that such fine-grained textual signals, when integrated with traditional financial indicators, can enhance the forecast accuracy of corporate earnings per share by up to 49% over models that rely solely on financial data. This underscores a key trend: the fusion of unstructured textual data with structured numerical data is creating more robust and accurate predictive tools.

However, the computational arms race this fuels, especially in high-frequency trading, has a substantial environmental cost due to the immense energy consumption of the required data centers. For instance, Huang et al. proposed LAMs in the financial field, Open-FinLLMs [81], including FinLLaMA, FinLLaMA-instruct, and FinLLaVA, which are used to process text, table and time-series data, and multimodal data, respectively. Among them, FinLLaMA uses 5.2 billion financial corpora for pretraining, FinLLaMA-instruct uses 573 K financial instructions for fine-tuning, and FinLLaVA uses 1.43 M image text instructions for training.

6.3. Enhancing Market Integrity and Financial Inclusion

Agentic AI systems powered by LAMs are automating core financial workflows, a process that bolsters market integrity, reduces operational costs, and crucially, has the potential to broaden financial inclusion [82]. This automation of core financial workflows is not merely theoretical; it is being actively implemented in areas like automated compliance and auditing. A compelling example is the automated financial voucher analysis. Research by Fuad et al. [83] presented a multi-stage framework combining Large Vision Models (LVMs) for document digitization with specialized financial LLMs for data extraction and anomaly detection. Their work shows that domain-specific financial LLMs outperform general-purpose models by a significant margin (+19.9% overall) in recognizing financial terminology and ensuring numerical accuracy. By automating tasks such as expense categorization and tax compliance verification directly from vouchers, this system was able to reduce false positives in anomaly detection by 15.7%, thereby enhancing operational integrity and reducing the need for manual oversight. This automation operates on two key fronts: streamlining internal operations and sharpening external surveillance.

At the operational level, platforms like Hyperbots (https://www.newswire.ca/news-releases/agentic-ai-platform-for-finance-amp-accounting-hyperbots-raises-6-5m-series-a-co-led-by-arkam-and-athera-ventures-856574093.html) (accessed on 10 September 2025) utilize domain-specific LAMs (e.g., HyperLM) to streamline complex back-office cycles such as procure-to-pay and order-to-cash. By integrating directly with enterprise resource planning (ERP) systems, they reduce manual intervention in tasks like invoice processing and tax validation. This not only significantly improves accuracy and efficiency but also strengthens compliance from the ground up by embedding rules directly into the workflow [84].

At the market-facing surveillance level, multimodal LAMs like FinLLaVA [79,81] enhance compliance monitoring and fraud detection. By processing a combination of text, tables, and even visual data (e.g., analyzing inconsistencies between a stock chart and a news report), these systems can identify suspicious anomalies in transaction records. They fuse knowledge graphs with vision-language models to analyze financial statements for potential fraud, generating audit-ready explanations and significantly reducing false positives in compliance checks [81].

This dual-pronged automation has a crucial social sustainability implication: by dramatically lowering the operational costs of underwriting and compliance, AI makes it more economically viable for financial institutions to serve small businesses and low-income individuals who were previously considered too costly to onboard. This fosters greater financial inclusion, turning operational efficiency into a direct enabler of social equity.

6.4. Sustainability Challenges and Future Directions in Financial AI

The deployment of LAMs in the high-stakes, hyper-connected world of finance introduces profound sustainability dilemmas. The reviewed literature presented a clear convergence on the efficiency gains AI offers, yet it also showed significant divergence on how to assess the robustness of these systems against systemic risks like AI-driven “flash crashes” or algorithmic bias. This contrast between short-term optimization and long-term stability risk will be a focal point of our cross-domain synthesis in Section 8:

• The Stability Paradox: Risk Mitigation vs. New Systemic Threats: While LAMs are designed to manage risk, their speed, complexity, and interconnectedness could inadvertently create new, faster-moving systemic threats. The review by Sai et al. [85] emphasizes that while Generative AI offers powerful tools for risk assessment and fraud detection, its ’black box’ nature poses a significant challenge to interpretability, which is a cornerstone of financial regulation and trust. This opacity can create new forms of systemic risk where model-driven decisions, if flawed, could propagate silently across the financial system. An AI-driven “flash crash,” triggered by algorithmic misinterpretations of news or coordinated bot activity, could destabilize markets in minutes. The homogeneity of popular foundation models used across the industry could also create single points of failure, where a flaw in one model leads to cascading errors across the system, threatening economic sustainability.
• The Equity Dilemma: Unbiased Decisions vs. Algorithmic Redlining: LAMs are often touted as a solution to human bias in lending and credit scoring. However, if these models are trained on historical data that reflect past discriminatory practices, they may learn and even amplify those biases. Sai et al. [85] reinforce this concern, noting that bias in training data is a primary challenge for GenAI in finance, potentially leading to discriminatory outcomes in areas like automated loan approval or insurance underwriting. Addressing these biases requires not only technical solutions but also robust data governance and ethical oversight frameworks to ensure fairness. This can lead to “algorithmic redlining,” where certain demographic groups are systematically denied financial services, not because of individual risk, but because of correlations present in the data. This poses a grave threat to social sustainability and risks using technology to create a new, more opaque form of discrimination.
• The Confidentiality–Performance Trade-Off: The financial industry is built on sensitive data, creating an acute tension between the need for data to train powerful models and the imperative to protect confidentiality. Centralized data training poses significant security risks and raises privacy concerns [86]. While solutions like federated learning offer a path forward, a breach of financial data can erode trust on a massive scale. Furthermore, “hallucinations” or errors in LAM outputs could lead to disastrous financial advice or non-compliant actions, making model robustness a critical concern [87].
• The Arms Race and the Environmental Burden: The quest for a competitive edge, particularly in algorithmic trading, has sparked a computational arms race [88,89]. This results in immense and growing energy consumption to power the data centers that train these increasingly complex models and execute trades in microseconds. This issue of high resource demand is a recurring theme in recent literature [85]. However, innovative solutions are emerging to mitigate these costs. For example, the hybrid framework proposed by Jehnen et al. [80] demonstrates a resource-efficient approach. By using an efficient sentence-transformer for the bulk of the workload and selectively engaging a more powerful RAG-LLM only for ambiguous cases (less than 10% of the data), they create a scalable yet powerful system. This highlights a promising future direction: designing hybrid, cost-effective models that balance performance with computational and, by extension, environmental sustainability. This direct environmental impact is a frequently ignored externality of the financial AI boom, posing a challenge to the industry’s broader environmental, social, and governance (ESG) commitments. The development of more energy-efficient models and benchmarks like FinBen are crucial steps toward a more sustainable financial AI ecosystem [84].

7. LAM Applications in the Transportation Domain

Transportation networks are the lifeblood of modern economies and societies, but they are also a primary contributor to greenhouse gas emissions and urban congestion—two of the most significant barriers to sustainable development. LAMs offer a dual potential in this domain: to orchestrate a smarter, cleaner, and more efficient mobility system, and conversely, to create new forms of energy consumption and social inequity [90]. This section reviews the applications of LAMs through a sustainability lens, evaluating how they can help build a more environmentally sound (low carbon), socially inclusive (access), and economically resilient transportation future, while also critically examining the trade-offs inherent in this technological shift.

7.1. Traffic Flow Prediction and Dynamic Management

LAMs excel at modeling complex spatio-temporal dependencies, making them ideal for predicting traffic patterns and optimizing network efficiency, which directly contributes to environmental sustainability [91,92]. By accurately predicting traffic, these models enable dynamic management that can significantly reduce congestion. For instance, PFNet [93], a progressive spatio-temporal fusion framework, integrates multi-view sequence encoders and graph embeddings to capture large-scale traffic dynamics, significantly improving prediction accuracy while reducing computational complexity compared to traditional methods. Similarly, ChatTraffic [94] combines text descriptions with historical traffic data using diffusion models, enabling the generation of realistic traffic scenarios for proactive management. Moreover, hybrid architectures, such as those leveraging graph neural networks (GNNs) and LAMs, dynamically adjust traffic signal timings based on real-time congestion data, reducing bottlenecks in urban corridors [95]. These advancements transform reactive traffic systems into adaptive, self-regulating networks, forming the backbone of a smart, green city.

7.2. Intelligent Accident Detection and Emergency Response

LAMs enhance road safety, a critical component of social sustainability and public health, by automating accident detection and accelerating emergency interventions. AI-powered systems, such as those developed by Hikvision (https://www.hikvision.com/cn/NewsEvents/Newsroom/2025/2025-04-28/?q=%E5%A4%A7%E6%A8%A1%E5%9E%8B&pageNum=1&position=3&hiksearch=true) (accessed on 10 September 2025), can analyze video feeds in real-time to identify collisions or stationary vehicles, enabling minute-level incident discovery and automated reporting. During emergencies, LAMs can synthesize data on vehicle trajectories and weather conditions to dynamically generate the safest evacuation routes and optimize resource deployment. Frameworks like the multi-agent reinforcement learning model TMABLPPO [96] help ensure low-latency responses in critical events by balanceing computational loads between autonomous vehicles and roadside units (RSUs). By improving coordination among first responders and mitigating human error, these systems build more resilient transportation ecosystems that protect human lives.

7.3. Autonomous Vehicle Coordination and BEV Mapping

LAMs are the cognitive engine driving the autonomous vehicle (AV) revolution, pivotal for enhanced environmental perception and decision-making. Models trained on vast, heterogeneous data from LiDAR and camera feeds improve obstacle detection and coordination between vehicles [97] (e.g., Vehicular Embodied AI Networks (VEANs) [96]). Moreover, multimodal models that integrate bird’s-eye-view (BEV) maps with semantic information enhance the ability of AVs to navigate complex intersections safely and avoid blind-spot collisions [33]. While this could lead to more efficient driving patterns and smoother traffic, the pursuit of full autonomy presents a profound sustainability dilemma. The development, training, and operation of these models require immense computational power, leading to a massive energy and carbon footprint. A true sustainability assessment must weigh the potential on-road efficiencies against the significant off-road resource costs of the AI that enables them.

7.4. Multimodal Data Fusion for Public Transit Optimization

LAMs streamline public transportation by synthesizing multimodal inputs (e.g., passenger flow, weather, event calendars), which is key to creating sustainable and equitable cities. For example, TransGPT [98], a domain-specific LAM, dynamically adjusts bus frequencies and routes based on real-time ridership patterns, reducing operational costs while maintaining service quality. JiaDu’s PCI Knowledge-Practice TransGPT (https://www.pcitech.com/en/transgpt.html) (accessed on 10 September 2025) is based on transformer technology [99], and utilizes LAMs’ multi-round dialogue, complex reasoning, data analysis, knowledge question and answer, and powerful content generation capabilities, combined with transportation industry-specific data and information, to provide the urban transportation industry with more intelligent, efficient, and real-time solutions and decision support, as well as a new human-like interactive experience to meet the growing needs of the urban transportation industry. This optimization not only reduces operational costs (economic sustainability) but, more importantly, improves service quality and reliability. By making public transit a more attractive and convenient alternative to private vehicles, this application helps reduce overall traffic congestion and emissions. Crucially, by enhancing affordable and accessible mobility options, this technology directly contributes to social equity, ensuring all members of a community can access jobs, education, and services.

7.5. Sustainability Challenges and Future Directions in Transportation AI

The vision of a smart, sustainable transportation system powered by LAMs is compelling, but the literature reveals conflicting outcomes. Findings consistently point to efficiency gains in traffic flow, yet the robustness of these gains is challenged by the immense energy demands of the underlying digital infrastructure—the “Efficiency Paradox”. Evaluating conflicting results on whether these systems create more equitable access or deepen the urban–rural divide is critical. These trade-offs pit optimization against broader sustainability goals, and we will synthesize this tension across all domains in Section 8:

• The Efficiency Paradox: Reduced Emissions vs. Increased Energy Demand: While AI-optimized traffic flow and AVs can reduce the fuel consumption of individual vehicles, the digital infrastructure required to run them is incredibly energy-intensive [96]. The constant data transmission, processing in large data centers, and the manufacturing of sensors and edge devices create a new, and often invisible, carbon footprint. Future research must focus on developing lightweight, energy-efficient “green AI” for transportation to ensure that we are solving the emissions problem, not simply displacing it.
• The Equity Divide: Smart Cities vs. Underserved Communities: The benefits of AI-driven transportation may not be distributed evenly. There is a significant risk that advanced technologies like AVs, smart signaling, and on-demand transit will be deployed primarily in affluent urban centers, while rural and low-income communities are left with deteriorating traditional infrastructure [100,101]. This could exacerbate existing mobility inequalities, creating a two-tiered system and undermining the goal of social sustainability.
• The ‘Black Box’ Dilemma: Safety vs. Accountability: In safety-critical applications like autonomous driving or emergency response, the “black box” nature of some LAMs poses a major accountability challenge. When an AI system makes a life-or-death decision, who is responsible? The lack of clear interpretability and accountability frameworks impedes regulatory approval and public trust, creating a significant barrier to the deployment of technologies that could otherwise save lives. This accountability vacuum is a fundamental sustainability issue because a socio-technical system that cannot ensure responsibility, learn from failures, or maintain public trust is inherently unstable and cannot be sustained over the long term.
• The Data Privacy vs. System Optimization Trade-Off: A fully optimized city-wide traffic network requires access to vast amounts of granular, real-time data from vehicles and personal devices. This creates an inherent conflict with individual privacy. Building robust, privacy-preserving data-sharing frameworks (e.g., using federated learning) is essential [97,102]. Without them, the public’s trust may be eroded, creating a social backlash that could halt the progress toward a more efficient and sustainable transportation system.

Having now systematically reviewed the applications, benefits, and domain-specific challenges of LAMs across medicine, agriculture, education, finance, and transportation, we provide a consolidated overview to synthesize our findings.

Table 1. Summary of representative studies on LAMs across pivotal livelihood domains.

Domain	Reference	Application Ask	Key Contribution
Medical	[9]	Privacy-Preserving Data Analysis	Demonstrates the critical role of FL in enabling collaborative analysis of large-scale healthcare data while preserving patient privacy, addressing a core data governance challenge.
	[46]	FL under Data Heterogeneity	Empirically shows that real-world data heterogeneity significantly degrades the performance of FL algorithms, highlighting a key sustainability challenge in achieving equitable and robust models across diverse healthcare institutions.
Agriculture	[50]	Plant Disease Detection	Integrates LLMs with Agricultural Knowledge Graphs to improve the efficiency and accuracy of plant disease detection, showcasing a data-driven approach to sustainable pest management.
	[52]	Coffee Leaf Disease Remediation	Employs RAG to overcome LLM limitations, achieving high precision in diagnosing crop diseases and providing context-aware treatment advice, thereby reducing false negatives.
Education	[73]	Competitive Programming Education	Reveals that human programmers still significantly outperform ChatGPT in complex programming tasks, providing a critical perspective on the limitations of current LAMs in high-level reasoning and creative problem-solving.
	[74]	Student Learning Preferences	Explores student choices between ChatGPT and human-curated platforms (Stack Overflow), concluding that a symbiotic “human–AI partnership” is the most effective model for learning complex skills.
Finance	[80]	Corporate Performance Prediction	Introduces a hybrid, resource-efficient framework that combines specialized transformers with a RAG-LLM, significantly improving prediction accuracy while addressing the high computational costs associated with large-scale financial analysis.
	[83]	Automated Voucher Analysis	Presents a multimodal framework using LVMs and financial LLMs to automate auditing tasks, demonstrating that domain-specific models enhance market integrity by significantly reducing errors in compliance checks.
Transportation	[33]	Intelligent Transportation Systems	Provides a comprehensive review of LAMs in transportation, covering applications from traffic flow prediction to autonomous vehicle coordination and highlighting challenges related to data privacy and system optimization.

summarizes the representative studies discussed throughout these five pivotal livelihood domains. This table offers a comparative snapshot, highlighting the diverse application areas and the key contributions of recent research, thereby setting the stage for the overarching analysis of systemic sustainability costs that follows in the next section.

8. The Sustainability Costs and Governance Challenges of the LAM Paradigm

Having reviewed the domain-specific applications of LAMs, this section takes a crucial step back to critically analyze the overarching sustainability costs that are inherent to the current paradigm of their development. These challenges are not mere technical side effects but are deeply embedded in the lifecycle of LAMs—from data acquisition and model training to large-scale deployment. They cut across the five livelihood domains examined in this review and represent systemic trade-offs that pit the promise of progress against the principles of environmental stewardship, social equity, and long-term economic resilience (while our analysis is grounded in these five domains, the systemic nature of these challenges suggests they likely have broader applicability across other sectors where LAMs are being deployed). A failure to confront these costs directly threatens to create a future where technological solutions generate more profound problems than they solve.

8.1. The Environmental Footprint: The Hidden Cost of Intelligence

The most direct and quantifiable sustainability challenge is the immense environmental footprint of LAMs. While these models are often used to optimize resource use in fields like agriculture and transportation, the digital infrastructure they depend on is profoundly resource-intensive, creating a significant environmental paradox.

• Energy Consumption and Carbon Emissions: The training of a single state-of-the-art foundation model requires a colossal amount of computational power, consuming electricity on the scale of a small city over several months. This results in a carbon footprint equivalent to hundreds of trans-atlantic flights [103]. Critically, this energy cost is not a one-time expenditure; the subsequent fine-tuning and the billions of daily queries (inference) globally create a continuous and growing energy demand, placing immense strain on power grids [104].
• Water Usage: Data centers, the physical homes of LAMs, are incredibly water-intensive. Billions of gallons of fresh water are used annually for cooling the massive server farms that power the AI boom [105]. This places significant stress on local ecosystems, particularly as many data centers are located in regions already facing water scarcity.
• Hardware Lifecycle and E-Waste: The relentless pursuit of greater computational power drives a rapid hardware upgrade cycle for specialized processors (GPUs/TPUs). The mining of rare-earth minerals required for this hardware is often environmentally destructive and fraught with ethical labor issues. Furthermore, the short lifespan of these components contributes to a growing global crisis of electronic waste (e-waste), one of the most toxic forms of refuse. A true sustainability assessment must therefore consider the entire lifecycle, from mine to landfill.

8.2. The Social Fracture: Amplifying Bias and Deepening Divides

Beyond the environmental costs, the social implications of LAMs threaten to fracture societies and deepen existing inequalities. LAMs act as powerful mirrors that not only reflect but also amplify and scale the biases present in their vast training data.

• Systemic Amplification of Bias: Models trained on internet-scale text and images inevitably absorb the historical and societal biases embedded within that data [103]. As demonstrated in our domain reviews, this can lead to inequitable health recommendations for certain demographics, discriminatory “algorithmic redlining” in finance, or the reinforcement of stereotypes in educational content. Unlike individual human bias, algorithmic bias operates at an unprecedented scale and speed, systemically disadvantaging entire communities under a false veneer of technological objectivity.
• The Global Digital Divide: The immense cost and technical expertise required to develop and deploy cutting-edge LAMs are creating a stark global divide. This leads to a “Matthew effect”, where affluent nations and large corporations reap the benefits of AI-driven productivity gains, while low-resource communities are left further behind. This threatens to create a world of AI “haves” and “have-nots”, exacerbating global inequalities rather than alleviating them [106].
• Concentration of Power: The astronomical costs of training foundation models have created a market where only a handful of trillion-dollar technology corporations can compete. This has led to an unprecedented concentration of power, stifling competition and creating a high dependency on proprietary, closed-source models. This quasi-monopolistic structure threatens to dictate the future of digital innovation and raises serious anti-trust concerns [106].

8.3. Epistemological Risks and the Erosion of Trust

Finally, the very nature of LAMs introduces profound epistemological risks—challenges to our ways of knowing, reasoning, and trusting information. These challenges are not merely philosophical; they represent a direct threat to social sustainability. A society cannot sustain itself over the long term if its foundational pillars, shared knowledge, public trust in institutions, and citizens’ cognitive capabilities, are systematically eroded. The following risks, therefore, undermine the very fabric of a resilient and functional society.

• The ‘Black Box’ and Accountability: As highlighted across all high-stakes domains, the lack of true interpretability in many LAMs creates a critical accountability vacuum. In medicine, finance, or autonomous transport, an inability to understand why a model made a specific decision makes it nearly impossible to assign responsibility when things go wrong, eroding clinical and public trust.
• “Hallucinations” and Information Pollution: LAMs’ tendency to generate confident-sounding but entirely fabricated information (“hallucinations”) poses a direct threat to our information ecosystem [107]. When deployed at scale, this can pollute public discourse with sophisticated misinformation, making it increasingly difficult for citizens to distinguish fact from fiction.
• Cognitive Offloading and De-skilling: An over-reliance on LAMs for cognitive tasks—from writing emails to generating code and creating educational content—risks a long-term “de-skilling” of the human population. As explored in the education section, if AI systems consistently provide answers rather than foster inquiry, they may undermine the development of critical thinking, creativity, and problem-solving skills, posing a risk to our long-term cognitive resilience.

Confronting these systemic challenges is the prerequisite for moving from the current, often unsustainable paradigm, towards a future where AI is developed and governed as a genuine public good. This sets the stage for our concluding recommendations.

9. Conclusions and Recommendations for a Sustainable AI Future

The evolution of LAMs, as explored in this review, unequivocally presents a double-edged sword for the future of sustainable development. On one hand, their capacity for general reasoning and multimodal interaction has unlocked unprecedented opportunities to address systemic societal challenges in healthcare, agriculture, education, finance, and transportation, potentially enhancing efficiency, equity, and quality of life. On the other hand, as our analysis of their underlying costs reveals, the current trajectory of their development poses significant threats to environmental stability and social equity.

This critical appraisal suggests that the pursuit of more powerful AI cannot be decoupled from its profound sustainability implications. A narrow focus on technological capability without a corresponding commitment to environmental stewardship and social responsibility risks creating ’solutions’ that are themselves unsustainable. Therefore, the central challenge for the next decade is not simply to build larger models, but to innovate towards a paradigm of ‘Sustainable AI’, AI that is not only intelligent but also resource-efficient, ethically aligned, and socially inclusive.

Moving forward, a concerted effort from multiple stakeholders is essential. For Policymakers: Establishing clear regulatory frameworks that mandate transparency in energy consumption and algorithmic impact assessments, while incentivizing research and development in green computing and smaller, more efficient AI models. For Technologists and Researchers: Shifting research priorities to include ’efficiency’ and ’fairness’ as core metrics of model performance, alongside accuracy. This includes developing novel architectures, federated learning approaches to protect data privacy, and robust methodologies for bias detection and mitigation. For Industry Leaders: Adopting a lifecycle approach to AI development that accounts for environmental and social externalities. This involves responsible data sourcing, investing in renewable energy for data centers, and committing to public education and workforce retraining programs.

To translate the vision of ‘Sustainable AI’ into a tangible research agenda, we propose several key future research directions:

• Quantifying the AI Lifecycle and Developing “Green AI” Benchmarks: Future work must move beyond fragmented estimations of AI’s environmental impact. A critical research direction is the development of a standardized Lifecycle Assessment (LCA) framework to quantify the complete environmental footprint of LAMs, from hardware manufacturing and data center construction to model training, inference, and disposal. In parallel, the research community should establish and promote “Green AI” benchmarks that elevate metrics like energy efficiency (e.g., performance-per-watt) and computational cost to be as critical as traditional accuracy scores, fostering innovation in resource-efficient algorithms such as model compression and quantization.
• Cross-Domain Metrics and Interventions for Algorithmic Fairness and Social Inclusion: Addressing algorithmic bias requires more than generic fairness metrics. Future research should focus on creating context-aware fairness frameworks tailored to specific livelihood domains, accounting for unique domain vulnerabilities (e.g., equitable access for rare disease patients in medicine). This includes advancing causal reasoning in LAMs to prevent decisions based on spurious correlations in historical data and exploring robust, privacy-preserving technologies like federated learning to empower low-resource communities and bridge the digital divide.
• Longitudinal Studies on Human–AI Cognitive Partnership and Resilience: The long-term societal impact of LAMs on human cognition remains largely unexplored. A crucial, forward-looking research agenda involves conducting longitudinal studies to assess the effects of sustained LAM use on critical thinking, problem-solving skills, and creativity in fields like education and science. The focus should shift from designing AI that merely provides answers to creating “intelligence-augmenting” AI that fosters inquiry and acts as an intellectual sparring partner. This also entails developing meaningful Explainable AI (XAI) to help users properly calibrate their trust in AI systems.

These research directives provide a concrete roadmap for a multi-stakeholder effort to steer the development of LAMs toward a more sustainable, equitable, and ultimately more beneficial future.

Ultimately, the journey from general intelligence to domain-specific adaptation must be guided by a more profound adaptation: aligning the trajectory of artificial intelligence with the long-term project of human and planetary well-being. The true measure of advanced AI will not be its ability to mimic human intelligence, but its capacity to help sustain it.