AI Wave in the Year of the Horse: A New Era for Intelligent Software Engineering

Start: Prepare Thoroughly to Achieve Success

Historical anecdotes about horses illustrate the importance of preparation; similarly, software projects often fail because the early phases lack clear requirements, defined goals, and risk assessment. The author cites industry data showing persistently high project failure rates and argues that AI can serve as a modern "feed for the horses" by automating intelligent requirement analysis, feasibility studies, and effort estimation based on massive user feedback, ticket logs, and competitor documentation.

Design: Unbridled Innovation Needs Experienced Guidance

Effective architecture requires both "wild imagination" and the wisdom of "old horses"—experienced architects. The article notes that generative AI can now extract design patterns from open‑source repositories and propose dozens of alternative architectures, citing a case where an AI‑driven analysis of a ten‑year‑old legacy system (over one million lines of code) produced a complete service‑dependency graph and refactoring roadmap that would have taken months manually. However, AI still struggles to create truly novel paradigms, so human architects must blend AI suggestions with original insight.

Development: Lead with AI, Yet Ride Side by Side

AI coding assistants such as Copilot, Cursor, and Claude Code have multiplied developer productivity, turning routine coding tasks into seconds‑long operations. Anthropic’s report shows that while developers use AI in about 60% of their work, fully autonomous AI tasks remain below 20%, a phenomenon the author calls the "collaboration paradox." The optimal model is a "dual‑rider" approach: AI generates initial drafts, humans review and refine, and AI handles repetitive chores while engineers focus on business logic and creative problem‑solving. The article also describes the emergence of AI "agent teams" that coordinate multiple specialized agents for front‑end, back‑end, and testing tasks.

Speed vs Quality: Accelerate Carefully, Avoid Blind Acceleration

AI‑driven rapid development has led to high‑speed releases, but the article warns of the "blind horse" danger. It recounts the Moltbook platform, which launched with over 1.5 million AI agents and was compromised in under three minutes, exposing 35 000 user emails—a classic case of Vibe Coding without security review. Additional evidence includes reports that 60‑70% of AI‑generated code contains high‑severity vulnerabilities and 90% exhibits code smells, and that open‑source projects like curl have suffered from AI‑generated spam vulnerability reports. The author stresses that AI can also enhance quality through continuous code review, automated security scanning, and AI‑augmented testing, but human judgment must still decide when to curb speed for safety.

Testing: Hard to Catch, Quality is Gold

Traditional testing is reactive; AI flips this by predicting bug‑prone modules, auto‑generating test cases, and even performing mutation testing. The piece highlights how AI enables true Test‑Driven Development: tests are generated from requirements before any business code is written, ensuring testing stays "ahead of the horse" rather than constantly chasing it. Small "paper‑cut" issues that were previously ignored are now routinely fixed thanks to AI‑driven triage, reinforcing the notion that incremental quality improvements accumulate to significant reliability gains.

Operations: Armored Defense of Digital Frontlines

AIOps delivers three core capabilities—proactive alerts, rapid diagnosis, and self‑healing. AI can spot anomalous log patterns (the "old horse" recognizing danger), compress fault diagnosis from hours to minutes, and automatically restart services, scale resources, or roll back versions. Yet the article warns that purely automated responses can become "dangerous horses" when malicious traffic mimics normal behavior; human operators must still interpret alerts, set appropriate mitigation policies, and balance AI‑driven speed with strategic security considerations.

Team: Recruit Talent, Seek the Right Judges

AI reshapes hiring by automating resume screening, coding assessments, and interview assistance, but the author argues that AI can only identify "qualified" candidates, not the "exceptional" ones. Drawing on the ancient story of Jiufang Gao, the article emphasizes evaluating deeper qualities such as systems thinking, growth potential, collaboration, and judgment under uncertainty. Moreover, the expanding definition of "developer" now includes non‑technical experts who, empowered by AI, can contribute to software construction.

Culture: Beware Misrepresentation, Embrace Calm Perspective

The piece cautions against "calling a deer a horse"—blindly trusting AI outputs without verification. It cites real incidents where unchecked AI‑generated code introduced severe security flaws and where AI‑crafted presentations led to poor technical decisions. The author advocates for critical thinking, consistent coding standards, and disciplined code reviews to avoid a "non‑horse, non‑donkey" codebase that lacks cohesion. Additionally, the risk of "horse‑riding‑by‑the‑flowers"—superficial skimming of many AI‑suggested architectures—is highlighted, urging depth over breadth.

Outlook: Expanding Horizons, Riding Forward

Anthropic predicts that AI agents will evolve from minute‑scale tasks to multi‑day autonomous system builds, with multi‑agent collaboration supplanting single assistants. The article foresees a future where "anyone can be a developer," but also stresses that each paradigm shift brings challenges: security vulnerabilities, hidden technical debt, evolving engineer roles, and the continual need for mature engineering culture.

Conclusion: A Journey Begins with Mastering the Horse

Closing with two historical analogies—a Chinese tale of Liu Bei’s horse rescuing him from a river and the Greek Trojan horse—the author reinforces that powerful tools (AI) are valuable when wielded wisely. In the AI‑driven software era, the "horse" (technology) can be a catalyst, but success ultimately depends on the rider’s wisdom, experience, and resilience.