The dawn of Artificial Intelligence (AI) has opened doors to limitless possibilities and, consequently, has exposed us to various challenges. For today's tech leaders, one particular AI-based development has turned heads: AI-generated code. As the boundaries of AI expand, Chief Information Officers (CIOs) and Chief Technology Officers (CTOs) must strategically harness this transformative technology while mitigating its inherent risks.

AI-generated code, put, is when AI models, typically machine learning, generate software code. Ground-breaking tools like GitHub's Copilot and OpenAI's Codex lead this wave, automating mundane coding tasks and generating whole applications. Such tools employ advanced language models trained on many codebases to output relevant, high-quality code for a given problem. 

The transformative power of AI-generated code is multifaceted. For one, it significantly speeds up software development. AI can generate thousands of lines of code in a fraction of the time a human programmer takes, cutting down development cycles and potentially saving significant costs. Moreover, it democratizes programming, lowering the barriers of entry to coding. Those without an extensive programming background can quickly generate functional code with AI support.

Another pivotal benefit is error reduction. AI algorithms are less prone to making mistakes, so the produced code is likely to contain fewer bugs. They can even identify and fix issues in existing code, improving software robustness and reliability. This leads to a reduction in debugging time and increases the efficiency of software maintenance. 

Despite these transformative abilities, AI-generated code is only a panacea for some software development woes. The inherent potential dangers and limitations pose real challenges that executives must confront.

One primary concern is the quality of AI-generated code. AI, at its current stage, might not fully grasp a project's context and nuances, potentially leading to inaccurate or inefficient code. Also, while AI can learn from large codebases, it does not understand whether the code it was trained on is high quality. Thus, AI learning and propagating lousy coding practices is risky.

Another significant concern is security. Given that AI models learn from publicly available codebases, there's a risk that they might inadvertently generate code containing vulnerabilities or reproduce proprietary code they were trained on, leading to potential intellectual property disputes.

Ethics and accountability pose yet another challenge. If AI-generated code malfunctions or causes damage, who is responsible? The programmer who used the AI, the developers of the AI tool, or the AI itself? This unprecedented territory presents complicated legal and ethical considerations that executives must address.

Moreover, there's a risk of over-reliance on AI for coding. While AI can automate routine tasks, it can never replace the creativity, intuition, and deep understanding of experienced developers. More than relying on AI might diminish these skills and the art of programming itself.

Companies must adopt a strategic, balanced approach to harness the power of AI-generated code while mitigating its risks. They should incorporate guidelines for using AI code generators, ensuring that developers review and understand the generated code. This aids in maintaining code quality and prevents the propagation of potential vulnerabilities.

Regular audits of the AI-generated code are crucial to ensure compliance with coding standards and to detect security vulnerabilities. CIOs and CTOs must also advocate for robust, transparent models for AI training. This helps ensure that the AI is learning from high-quality, secure codebases. 

Ethical guidelines for AI use in coding should also be instituted, addressing accountability issues. The development of AI governance frameworks that detail the responsibilities of all stakeholders can be a significant step in this direction.

As the curtain rises on AI-generated code, CIOs and CTOs stand at the forefront of an era that promises to reshape the coding landscape. While the power of this technology is undeniable, it comes with potential dangers that must be addressed with foresight and strategic planning. Navigating this delicate balance is the challenge – and opportunity – that lies ahead for tech leaders.