The Dark Reality of AI Coding

The leak of Anthropic’s internal model, Mythos, has stirred up a commotion in the tech community. Many are trying to assess how much smarter it is compared to Claude 4.6 Opus. However, the most important takeaway from the Mythos leak is that AI coding is rapidly becoming a necessary poison for all product research and development teams.

This poison has a harsh formula: if you don’t use it, your progress will fall behind; if you do use it, your team’s coding skills and engineering intuition will irreversibly degrade.

The Necessity of AI Coding

In a world where tools like Cursor combined with Claude can enable a product manager to create a usable demo in just one afternoon, traditional agile development rhythms seem outdated. When competitors begin using AI to build features at a fraction of the cost and ten times the speed, discussions about code cleanliness become irrelevant. You must consume this poison to ensure you remain in the game.

The Toxicity of AI Coding

However, the toxicity manifests quickly. The core issue is that AI lacks self-boundary awareness; if you don’t set boundaries, it will endlessly pile on code. AI does not understand what it can or cannot do. When you present it with a new requirement, an experienced human engineer would pause to evaluate: “The current architecture can’t support this feature; we should refactor before adding new logic.” But AI will not do this. If you ask it to add a feature, it will simply patch the existing logic. If you ask it to write a button, it will hard-code one; if you ask it to connect to a database, it will insert request logic directly into the frontend component. As long as you don’t stop it, it will tirelessly continue to pile on code until the entire project becomes an incomprehensible mess.

This is the death spiral brought by Vibe coding. Initially, project progress is rapid. However, as the codebase expands quickly, it soon exceeds the effective context window of the AI. At this point, the AI can only see a portion of the code. If you ask it to fix a bug, its modifications based on this limited view can silently trigger a global collapse. To fix this new collapse, it generates even more patch code, leading to further project bloat.

Once this death spiral begins, it is essentially irreversible, and even the AI cannot save itself.

Vibe coding does enable many people to write code, but they do not acquire the ability to deliver software. The gap between a locally runnable demo and a production-level application that can be deployed, maintained, and withstand concurrent usage is not just about code volume; it involves architecture design, security boundaries, deployment strategies, and maintainability—areas where AI currently lacks foundational engineering capabilities.

The Monopoly of Claude

In the current AI programming tool ecosystem, whether it’s Cursor, Cline, or various IDE plugins, the underlying computational engine is almost monopolized by Claude 4.6 Opus. The leak of Mythos indicates that Anthropic continues to widen this gap in leading advantages.

This monopolistic lead grants Anthropic terrifying pricing power. The core selling point of the SaaS era is “building it yourself is too expensive and slow; it’s better to buy off the shelf.” AI coding has driven the cost of building down to the floor, destroying the cost moat of traditional SaaS. However, it has established a new moat: absolute dependence on top models.

When your team becomes accustomed to the generation quality of Claude, and your messy code becomes so complex that only Claude’s long-text reasoning ability can barely untangle it, you cannot afford to switch to open-source models or other competitors just to save a few bucks on API fees.

Switching models means a break in contextual understanding, leading to a collapse of previously functional code. Anthropic is no longer selling tokens; they are selling a tax on the R&D progress of the entire software industry. As long as they maintain their lead in coding capabilities, product research and development teams will have no choice but to pay up, as the cost of withdrawal is too high.

Redefining Roles in AI Collaboration

In light of this trend, product research and development teams and developers need to redefine their roles. Rejecting AI coding is futile; you cannot resist the crushing efficiency. However, you must clearly understand what your responsibilities are in this human-AI collaboration.

Since AI lacks self-boundary awareness, the core value of humans is to define these boundaries. The future senior engineer will no longer be the fastest typist or the one who memorizes various API syntaxes, but rather the one who can keenly sense when “the code starts to smell,” decisively hit the pause button, and direct the AI to refactor. You need to control the software architecture, break down reasonable modules, manage the context size of each module, and ensure that AI generation occurs within a safe, controllable sandbox.

You must become the one who sets boundaries for AI. Otherwise, you will be buried under the mountains of code that AI generates.