At Red Chalk Group, we are noticing increased interest among clients in how DeepSeek might impact the AI landscape, such as its impact on facility buildout, rack density, power needs, and the industries that will experience the greatest impact.
As the landscape rapidly evolves, DeepSeek’s latest advancements mark a significant inflection point. The emergence of DeepSeek-R1 could cause the following industry shifts:
First-Order: Lower Barriers to Entry, More LLM Entrants
DeepSeek-R1 may demonstrate that AI models can operate effectively on less advanced, lower-cost microchips, thereby reducing computing expenses. Its open-source approach could enable broader collaboration, making AI more accessible and fostering new LLM entrants beyond traditional hyperscalers (e.g., Google). Additionally, open-source models might shift the focus from extensive training toward increased inferencing, as companies build upon each other’s models.
Second-Order: Rising Compute Demand, Data Center Growth, and Faster Development
As AI becomes cheaper and more efficient, Jevon’s paradox indicates that compute demand might increase. This could drive further expansion of data centers, potentially shifting from large hyperscale facilities to more distributed colocation and edge computing models – leading data center investors (e.g., Blackstone) have stated they expect to continue investing as major customers have indicated they plan to stay the course (Financial Times). Open-source knowledge sharing and reduced training needs might also accelerate AI development, pulling innovation forward by several years.
Third-Order: Geopolitical Shifts, Increasing Power Demand, and Intelligent Operation
DeepSeek’s emergence from China might indicate a geopolitical shift in AI leadership, reducing reliance on Silicon Valley and opening competition to new global players. Increased AI usage could drive higher energy demand, potentially leading to greater investment in on-site power solutions like solar and nuclear SMRs. As AI becomes more affordable, adoption may accelerate across industries. Over time, AI may evolve beyond standalone tools and agents into intelligent operations, where systems autonomously optimize and manage complex tasks in domains like logistics, transportation, and industrial automation.
