When AI begins to attempt building a complete web browser from scratch—including HTML parsers, CSS layout engines, and even a self-developed JavaScript virtual machine—it faces not only code generation but also a rigorous test of logical consistency, task persistence, and engineering understanding.
Recently, the well-known AI coding tool Cursor released an impressive internal test result: OpenAI's latest model, GPT-5.2, significantly outperformed Anthropic's Claude Opus 4.5 in long-term, high-complexity autonomous programming tasks, demonstrating unprecedented engineering-level reliability.
This experiment was not simply about piecing together code snippets, but required the model to continuously advance a system-level project involving millions of lines of code over several weeks. During this process, the AI had to repeatedly understand the context, correct early design flaws, coordinate module dependencies, and always keep the final goal in focus. The test showed that GPT-5.2 could reliably follow complex instruction chains, with almost no "goal drift"—a common issue where the AI deviates from the original task intent—during long-term reasoning. Although Claude Opus 4.5 performed well in short-term question answering and single-file coding, it tended to terminate tasks prematurely, seek simplification paths, or hand control back to humans when facing such "marathon-style" engineering challenges.
This difference highlights a key dividing line in current large models' "autonomous agent" capabilities: whether they can continue large-scale projects independently, like human engineering teams. The Cursor team pointed out that GPT-5.2 not only completed the browser construction, but also successfully replicated a Windows 7 simulator and led a legacy system migration task involving over a million lines of code—work that originally required months of human effort is now being gradually taken over by AI with remarkable coherence.
Currently, GPT-5.2 has been integrated into the Cursor platform, allowing developers to directly call its capabilities for advanced programming collaboration. This move not only improves individual development efficiency but also hints at a new paradigm: in the future, AI may become a "digital engineer" capable of independently undertaking end-to-end software engineering. When models are no longer just assisting in writing functions, but can plan architectures, debug systems, and iterate optimizations, the boundaries of software development are being completely redefined.
