Person without substrate: individuation of LLMs and regime

The advancement of large language models (LLMs) has opened a fundamental debate on how to interpret their internal representations. Recent research indicates that the same vector direction can acquire different meanings depending on the usage regime: during instruction adaptation, supervised fine-tuning, or inference-time manipulation. This variability challenges the traditional notion that a fixed content is associated with each unit of representation. For companies integrating artificial intelligence into their processes, understanding this plasticity is critical: it is not enough to train a model; one must know how it will behave under different operating conditions. Q2BSTUDIO, as a software and technology development company, addresses these challenges by offering AI for businesses that go beyond simple model implementation. By designing custom applications, our teams consider the influence of the interaction regime to ensure consistency and robustness in results. This approach is complemented by AWS and Azure cloud services that allow models to be scaled in a controlled manner, and by business intelligence services such as Power BI to visualize system behavior. Additionally, cybersecurity and orchestration of AI agents are part of a comprehensive strategy that ensures each custom software solution not only works but is predictable and reliable. The regime-based individuation proposed in the literature reminds us that the identity of an AI content depends on the context of use; in business practice, this translates into the need to audit, version, and monitor each interaction. At Q2BSTUDIO, we apply these lessons to develop systems that not only execute tasks but do so with the transparency and alignment required by the real world.