Application workload slows down first
As AI agents automate more steps in everyday workflows, the work does not need to be performed within a large suite of applications. This indicates slow growth in data center demand for enterprise resource planning, customer relationship management, human capital management and supply chain management software. Reasoning-Model Agent and Deep Search Tool can now autonomously browse the web, extract sources, and perform analysis.
Engineering software – computer-aided design and computer-aided manufacturing – can skirt these headwinds as simulation and synthetic data creation maintains workloads where specialized tools are fixed.
Coding Agent Super Charge Test Workload
AI Coding Agents – Assistants within developer tools to suggest, write and modify code – should significantly improve application development and test workloads. Cursor's agents, Anthropic's Claude Code, Github Copilot, Openai's Codex, and Gemini code help with debugging and app-like handle tasks to existing code. Companies report a 30-40% productivity gain in new code written with these agents. Prompt-based code generation is becoming one of the most commonly used generation-AI features in existing business applications.
Content delivery and cybersecurity also bring benefits
As autonomous AI agents connect to business workflows, more mission-critical tasks are performed in the AI data center. The rise of inference models like Openai's O3 shifts focus from simply having a model to ensure that the infrastructure is fast, efficient and reliable. This is a tailwind for content delivery networks (CDNs) for companies such as CloudFlare and cybersecurity providers such as Zscaler. Most companies are trying to integrate LLMS with internal knowledge databases and documents with LLM, relying on CDNs and cybersecurity vendors to manage LLM tweaks and speculation token consumption.
