The growing body of Artificial Intelligence (AI) and Machine Learning (ML) models increases the necessity for computational needs that put the onus on looking for new approaches so as to stray from the traditional reliance on Graphics Processing Units (GPUs). This paper introduces a new method to maximize Central Processing Units (CPUs) through the use of a micro-containerization concept. The proposed approach theoretically dissects the CPU cores into isolated, efficient processing units called 'micro containers', making an effort to simulate GPU capabilities for parallel processing in order to enhance processing efficiencies to supposedly compete with GPU-based environments. Through a theoretical study of the possibilities in architecture and dynamics of operations, the paper shows how micro containerization can contribute to the democratization of advanced computational resources. The presented micro containerization model has the potential to revolutionize the landscape of machine learning workloads for the same very reasons: it is scalable, cost-effective, and holds a promise of maybe even changing the way computation strategies currently deal with tasks that are data-intensive. This bridges the current constraints with the future needs of computation, making micro containerization the new paradigm in accessibility and sustainability of high-performance computing innovation for a wide class of AI applications.

The narrative that emerges from the current development and training of the artificial intelligence models has a rhetoric of unlimited potential of AI in its current state. This paper argues against that very point and will be looking at the serious constraints currently facing models of AI and machine learning, particularly the finitude of the data on which they rely. Its not just that the results are limited by the data at hand, but also by a host of other constraints: diminishing returns in scaling up model size, imposing costs on training, and making models harder to run. These are increasingly observed when AI is clad upon complex, real-world tasks that involve genuine generalization and adaptability. The paper also critically examines the commercial motivations driving the focus on automating white-collar jobs, highlighting that such priorities often stem from immediate financial incentives rather than a comprehensive evaluation of AI's broader applications. These practices raise profound ethical issues, including concerns about transparency and the responsible deployment of AI technologies. By dispelling the myth of AI's unlimited potential, this paper advocates for a more grounded and ethical approach to AI development, stressing the urgent need for breakthrough technological advancements that move us closer to achieving artificial general intelligence (AGI).