The most significant development in the artificial intelligence domain in early 2025 is the maturation of the AI agent model. As designated by Gartner, AI agents have emerged as the top strategic technology trend for the year. Unlike the generative models that defined the previous years, which primarily served as co-pilots or assistants responding to human queries, AI agents are designed to operate with a new degree of autonomy. These are defined as intelligent software components capable of making decisions and executing actions on their own to achieve a predefined objective, with little to no human intervention. Their purpose is not merely to generate content but to perform complex tasks, automate customer experiences, and accelerate decision-making processes for organizations.
This evolution represents a clear progression from the period of “hyper-experimentation” that characterized the previous 18 months, as noted by IDC. Businesses are no longer simply testing out AI tools; they are strategically integrating them into their core operations to fundamentally “reinvent” how they function. The scale of this commitment is reflected in the significant allocation of capital: 67% of the projected $227 billion AI spending in 2025 will come from enterprises embedding these capabilities directly into their operational workflows. This is a clear signal that the market is moving past a fascination with basic productivity enhancements and is now focusing on structural transformation.
The transition from query-based GenAI to goal-oriented Agentic AI can be understood as a conceptual shift in the very purpose of artificial intelligence. Previous iterations were primarily a linguistic phenomenon, where models like large language models (LLMs) were celebrated for their ability to read, write, and converse. However, the current phase is defined by a shift from “talking to doing”. This new focus is enabled by technological advancements that allow agents to execute code, integrate with other software via APIs, and control physical robotic systems. This change in emphasis from “what can AI say?” to “what can AI do?” fundamentally changes its application and its potential for business value creation. For example, the same underlying model that can generate a product description could, as an agent, autonomously manage an entire marketing campaign, from content creation to targeted ad placement and performance monitoring.
The proliferation of autonomous AI agents and their associated workloads has created an unprecedented demand for computational power, triggering a hardware renaissance that is poised to define the technology sector in 2025. After years of software dominance, hardware has reclaimed the spotlight, largely due to the massive energy and resource requirements of the AI revolution. The surging demand for compute-intensive workloads from GenAI, robotics, and immersive environments is exposing the limitations of existing global infrastructure, from data center power constraints to physical network vulnerabilities. This has even spurred a new trend in high-energy computing, with growing interest in “nuclear power for AI infrastructure” as traditional renewable sources prove insufficient to meet the escalating needs of tech giants.
The intensity of this demand has reshaped the competitive landscape for AI-specific hardware. While NVIDIA holds a commanding position with approximately 80% of the AI accelerator market , the market is no longer a monopoly. Competitors are employing distinct strategies to carve out market share:
AMD is making a significant push with its Instinct MI300 series, which is designed for large-scale generative AI models and is positioned as a viable alternative to NVIDIA’s high-end offerings.
Intel is targeting cost-conscious enterprises with its Gaudi AI chips, which it claims are up to 50% cheaper than NVIDIA’s H100 GPU. This strategy aims to capture a market segment that prioritizes cost-effectiveness over absolute performance.
Hyperscalers such as AWS, Google, and Microsoft are developing their own custom silicon, known as Application-Specific Integrated Circuits (ASICs), to reduce their reliance on third-party vendors like NVIDIA and control the soaring costs of training and inference workloads.
Startups like Cerebras and Tenstorrent are also attracting significant venture capital, challenging the established players with novel architectures and specialized solutions tailored for specific workloads.
The evolution of the hardware market is characterized by a simultaneous push for both extreme scale and targeted specialization. On one hand, the need for massive, centralized data centers to train frontier models is growing rapidly, with training compute doubling every five months. On the other hand, there is a parallel acceleration of innovation “at the edge,” with lower-power technology being embedded into devices ranging from smartphones to industrial robots and cars. This bifurcation is a strategic response to the physical and economic limits of a purely centralized, cloud-based model. It fosters a causal loop where the immense cost and compute demands of large models necessitate the development of more efficient and accessible specialized models, or “micro LLMs,” that can run on-device.
This hardware race has transcended commercial competition to become a matter of geopolitical significance. The push for custom silicon and domestic chip fabrication is no longer just about gaining a market edge; it is a strategic maneuver to reduce exposure to geopolitical risks and secure national control over the next wave of value creation. Leaders are now considering a geopolitical lens when sourcing ideas from allied innovation ecosystems and designing resilient supply chains from the start.
