arXiv:2603.14147v2 Announce Type: replace-cross 
Abstract: The generative artificial intelligence (AI) ecosystem is undergoing rapid transformations that threaten its sustainability. As models transition from research prototypes to high-traffic products, the energetic burden has shifted from one-time training to recurring, unbounded inference. This is exacerbated by reasoning models that inflate compute costs by orders of magnitude per query. The prevailing pursuit of artificial general intelligence through scaling of monolithic models is colliding with hard physical constraints: grid failures, water consumption, and diminishing returns on data scaling. This trajectory yields models with impressive factual recall but struggles in domains requiring in-depth reasoning, possibly due to insufficient abstractions in training data.
  Current large language models (LLMs) exhibit genuine reasoning depth only in domains like mathematics and coding, where rigorous, pre-existing abstractions provide structural grounding. In other fields, the current approach fails to generalize well. We propose an alternative trajectory based on domain-specific superintelligence (DSS). We argue for first constructing explicit symbolic abstractions (knowledge graphs, ontologies, and formal logic) to underpin synthetic curricula enabling small language models to master domain-specific reasoning without the model collapse problem typical of LLM-based synthetic data methods.
  Rather than a single generalist giant model, we envision "societies of DSS models": dynamic ecosystems where orchestration agents route tasks to distinct DSS back-ends. This paradigm shift decouples capability from size, enabling intelligence to migrate from energy-intensive data centers to secure, on-device experts. By aligning algorithmic progress with physical constraints, DSS societies move generative AI from an environmental liability to a sustainable force for economic empowerment.

يشهد نظام الذكاء الاصطناعي التوليدي تحولات كبيرة تهدد استدامته، حيث يؤدي الانتقال من النماذج البحثية إلى المنتجات ذات الحركة العالية إلى زيادة العبء الطاقي للاستدلال المتكرر. تتفاقم هذه التحولات بسبب نماذج التفكير التي تضخم تكاليف الحوسبة بشكل كبير لكل استعلام، مما يبرز التحديات التي تواجه نماذج اللغة الكبيرة (LLMs) في المجالات التي تتطلب تفكيرًا عميقًا.

El ecosistema de inteligencia artificial generativa (IA) está experimentando transformaciones significativas que amenazan su sostenibilidad, ya que el cambio de prototipos de investigación a productos de alto tráfico aumenta la carga energética de la inferencia recurrente. Este cambio se ve agravado por modelos de razonamiento que inflan drásticamente los costos de computación por consulta, destacando los desafíos que enfrentan los grandes modelos de lenguaje (LLMs) en dominios que requieren razonamiento profundo.

L'écosystème de l'intelligence artificielle générative (IA) connaît des transformations significatives qui mettent en péril sa durabilité, alors que le passage des prototypes de recherche à des produits à fort trafic augmente le fardeau énergétique de l'inférence récurrente. Ce changement est aggravé par des modèles de raisonnement qui gonflent considérablement les coûts de calcul par requête, mettant en lumière les défis auxquels sont confrontés les grands modèles de langage (LLM) dans des domaines nécessitant un raisonnement approfondi.

The generative artificial intelligence (AI) ecosystem is experiencing significant transformations that jeopardize its sustainability, as the shift from research prototypes to high-traffic products increases the energetic burden of recurring inference. This shift is compounded by reasoning models that dramatically inflate compute costs per query, highlighting the challenges faced by large language models (LLMs) in domains requiring deep reasoning.