Will AI Make Performance Testing Roles Obsolete?

Yesterday a colleague from the community asked whether the growing capabilities of AI, including AI‑integrated IDEs that offer performance analysis and optimization suggestions, mean that dedicated performance testing positions should be eliminated in favor of business testing roles.

AI Capability Evolution

The author traces AI development from a simple Q&A assistant in 2022, through multimodal abilities in 2023, to the emergence of agents and workflows in 2024, and finally to the current "Self‑Agent" stage characterized by extended thinking.

Extended Thinking (Interleaved Reasoning and Tool Use)

Extended Thinking was first described by Anthropic in Claude as a mechanism that lets the model perform internal reasoning before exposing necessary information. Technically, it means the model alternates between explicit reasoning and tool invocation, preserving and passing the reasoning state across multiple interaction rounds. MiniMax highlighted this "Interleaved Thinking" capability when releasing the M2 model in October, emphasizing its importance for agent‑based and coding scenarios.

Why Performance Testing Still Needs Humans

Despite AI’s progress, the author argues that performance testing will not be fully replaced for at least three to five years, especially for engineers with deep hands‑on experience, strong business‑domain knowledge, and a solid grasp of system architecture.

AI’s current value is limited to the execution phase. It can automate load‑test execution, monitor performance, analyze data, and generate reports, thereby improving efficiency but not handling the strategic aspects of testing.

Pre‑test planning consumes the most time. Defining load‑test goals, acceptance criteria, and the three core performance‑testing models requires extensive effort and long‑term investment, which AI cannot yet streamline.

Planning is a communication‑heavy activity. Coordinating requirements, aligning stakeholders, and negotiating trade‑offs dominate the workload; AI lacks the ability to manage these interpersonal dynamics effectively.

System and workflow changes introduce massive context. Each version iteration alters business processes and architecture, creating a huge token burden for LLMs. Without precise, up‑to‑date prompts, AI cannot reliably produce accurate testing strategies.

Additional considerations include the inherent hallucination risk of large models and the substantial effort required to build private, domain‑specific models—both needing large knowledge bases and extensive data collection.

Conclusion and Advice

AI is not as omnipotent as hype suggests, but its future remains promising. Professionals should stay curious, leverage AI tools to augment daily work, and continuously deepen their expertise to avoid being outpaced by those who master AI‑assisted workflows.