Alibaba’s new real-task benchmark delivers a number worth watching: the best-performing coding agent fully resolves only 26.4% of tasks, despite scoring above 99% on older benchmarks. Our judgment is that model capability is no longer the biggest bottleneck. System integration, access control, and manual cleanup in enterprise delivery are where the real costs lie.
What this is
Co-Design Bench tests user requests that previously failed in real environments, making it closer to actual enterprise operations than conventional programming challenges. Agents—AI programs that autonomously break down and execute multistep tasks—must handle legacy code, dependencies, and exceptions rather than merely generate a snippet of runnable code.
Related product developments point to the same problem. Tencent is connecting applications, data, and engineering infrastructure through WorkBuddy, Buddy One, DataBuddy, and other tools. Cloudflare has introduced Auto Router to select models automatically based on task complexity, ambiguity, and risk. Meanwhile, a Claude Code option that suppresses follow-up questions exposed credentials in a public code repository, affecting 1,122 files—a reminder that efficiency gains cannot come at the expense of security boundaries.
Industry view
We believe the competitive focus in 2026 is shifting from “whether the model can answer” to “whether the system can deliver reliably.” The enormous gap between legacy benchmarks and real tasks will force enterprises to rethink purchasing criteria. End-to-end completion rates, human handoff counts, access audits, and data integration costs are more meaningful than polished demos.
That does not mean the foundation-model path has lost its value. Apple could launch a transactional Siri that handles tasks on users’ behalf as early as next year, showing that entry-point products still have room to grow. TSMC is also reportedly considering entry into 3D DRAM, underscoring continued strength in compute demand. The risks are equally clear: investment in world models is cooling, video generation produces a satisfactory result only around one in ten attempts on average, and Anthropic is facing valuation downgrades and the loss of key personnel. Technical hype and commercial returns are beginning to separate.
Impact on regular people
For enterprise IT: Procurement will place greater emphasis on integration with existing systems, access controls, and audit mechanisms. Solutions that work across real workflows will be more credible than solutions with high scores on isolated leaderboards, and deployment cycles may also become longer.
For individual careers: Repetitive coding and document organization will increasingly go to agents, but validation, contextual explanation, and exception handling will remain human responsibilities. The ability to define the problem and verify the results will matter more than knowing how to write prompts.
For the consumer market: If an agentic Siri proceeds as planned, AI will evolve from an answering tool into an interface for delegating tasks. At the same time, credential leaks and unstable generated content will push vendors toward stronger permission prompts, paid security layers, and mandatory human confirmation.