Our perspectives
Here’s how the Xhilon business model is not only relevant but essential component for businesses embracing AI
Specialized Models and Orchestration
The world is shifting towards more specialized AI models, and the ability to connect and orchestrate these models in an agentic AI framework is crucial, especially in enterprise settings. This architectural shift means models will become smaller and more efficient, as seen with DeepSeek's R1 models and Perplexity's AI inference approach. Orchestrating these models allows for greater functionality, adaptability and scalability.
Parallelization and Distributed Training
Parallelization and distributed approaches to AI training are already essential and will continue to be so. Beyond leveraging hyperscale data centers, other systems and approaches will emerge to meet the growing demands of AI workloads. Utilizing the spare cycles on existing hardware is a natural business evolution in this environment.
Onboard AI Capabilities in PCs
PCs are becoming inherently AI-capable, with predictions suggesting that within two years, it will be impossible to buy a PC without AI capabilities. The aggressive phase-out of Windows 10 in 2025 is driving hardware upgrades, with many CIOs opting for PCs equipped with Neural Processing Units (NPUs) and other AI acceleration hardware.
AI Software and Hardware Optimization
The industry is under pressure to reduce the cost of goods sold (COGS) for running AI and is investing heavily in software and hardware evolution and optimization. As Satya Nadella noted in the Microsoft January 2025 earnings call, software optimizations can lead to significant improvements in AI efficiency (10x in each generation). This evolution will enable a broader range of hardware to do "complex" AI work.
Hyperscalers' Business Model Challenges
Hyperscalers' cost models are based on traditional transaction loads, which are small and bursty compared to AI workloads. The complexity and resource intensity of AI workloads make them very expensive for enterprises when they start using more AI in their normal operations. This will force companies to reconsider their approaches and explore alternatives like in-house solutions.
Privacy and Security Considerations
Privacy of data and intellectual property is a critical concern for enterprises, compounded by the fact that 40% of enterprises have experienced AI-related privacy breaches. Additionally, 63% of organizations have limited the types of data that can be entered into general AI tools, highlighting the need for secure and private AI solutions.
These factors underscore the relevance of Xhilon's' business model in addressing the evolving needs of corporate PCs in the AI era, particularly in terms of specialized model orchestration, distributed computing, onboard AI capabilities, cost optimization, and data privacy.
