07/01/2025
AI’s Power Problems
1) Datacenters Go Dark
Power shortages are set to become a significant challenge for datacenters, leaving racks empty as operators struggle to keep up with the rising demand for GPUs. To address the growing AI surge, some operators may relocate closer to power stations or even construct their own power plants. By year’s end, the conversation around scaling AI deployments will heavily focus on power shortages and the critical need for energy efficiency. While new innovations will offer some relief, governments and utilities will face increasing pressure to fast-track improvements to power grids.
This issue is already evident as Nvidia’s Blackwell chips pose considerable deployment challenges due to their high power density, leaving many datacenters unable to support them effectively.
2) Efficiency Takes Center Stage in Boardrooms
As power shortages escalate, enterprise leaders will face mounting scrutiny from their boards to ensure AI deployments are energy-efficient. Balancing return on investment (ROI) with rising power costs and carbon emissions goals will become a top priority.
Agentic AI and Inference
3) Inference Takes the Lead
In 2025, inference workloads will surpass training AI models as the dominant focus in AI. With the mainstream adoption of real-time AI applications, cloud and datacenter operations will shift heavily toward inference, driven largely by the growth of Agentic AI.
4) Agentic AI Unleashed
Autonomous AI agents are set to revolutionize tasks with minimal human intervention. Thanks to recent 10x improvements in large language model (LLM) speeds and specialized hardware, these agents will plan, reason, and process real-time information. They will handle complex, multi-step projects by leveraging diverse inputs to execute tasks with high efficiency.
5) The GPU Faces Competition
The rise of inference workloads and Agentic AI will introduce new challenges for GPUs, disrupting their dominance in AI hardware. Alternative, power-efficient hardware solutions better suited for inference will emerge, reshaping the market. As a result, Nvidia’s market share may shrink by up to 5%, potentially reducing its revenue by $10 billion. This shift will highlight the existence of superior platforms for inference, triggering a market adjustment.
Open-Weight Models Rise
6) Open Models Dominate
By 2025, open-weight AI models will surpass proprietary alternatives in adoption, driving innovation and accessibility across industries. Meta’s Llama 4 is expected to rival GPT-5 in capabilities, with debates over their superiority becoming largely academic. With widespread adoption, the Llama series could reach a billion downloads by the summer, solidifying open-weight models as the industry standard.
7) Sovereign AI Expands Globally
More nations and enterprises will prioritize developing their own sovereign AI systems as a strategic necessity. These systems will secure economic futures and maintain international competitiveness. While the U.S. may not immediately build its own national AI, it will treat AI as a tool for geopolitical leverage, leading to rare bipartisan policy initiatives in Congress.
Big Advances in Memory
8) Memory Optimization Drives AI Forward
In 2025, advancements in memory technology and model architectures will enable systems to retain extensive contextual information. This development will enhance Agentic AI’s ability to anticipate, plan, and execute with autonomy. The growing demand for contextual awareness will make memory optimization a cornerstone of AI development.
9) Energy Efficiency Redefines AI Hardware
Optimized memory architectures will play a crucial role in reducing energy consumption, lowering operational costs, and alleviating datacenter power demands. As power shortages intensify, hardware vendors will prioritize energy-efficient solutions to remain competitive and sustainable.
