For several months now, new models have been able to operate autonomously for hours and generate rather complex projects.

AI is Burning Money
https://www.tomshardware.com/tech-industry/artificial-intelligence/amazon-accidentally-spent-usd1-8-million-using-claude-for-menial-coding-task-went-860-percent-over-budget-catastrophically-expensive-coding-blunders-discovered-in-internal-amazon-ai-usage-metrics
Amazon accidentally spent $1.8 million on Claude Sonnet for a simple task (matching authors with products). The budget was exceeded by 860%, the error went unnoticed for 5 months, and the project was never launched. A bug like this, which previously would have cost pennies, now burns hundreds of thousands of dollars due to token-based pricing. Naturally, no refunds are provided—the riches go to the providers, who tempt users in their advertisements to deploy more and more autonomous agents.

Software Continues to Degrade
https://ptrchm.com/posts/nothing-works-and-everyone-is-euphoric/
The author ironically describes the current wave of AI enthusiasm as a form of mass psychosis. We are constantly promised that artificial intelligence will radically boost productivity, automate programming, and deliver higher-quality software. But if code generation is practically a solved problem, why does the software around us continue to get worse?

Updates spark anxiety, user interfaces degrade, and bugs multiply—a banking app that constantly requires re-authorization; Slack stealing window focus; the LG website where the warranty claim form breaks at the very last step; or an in-car multimedia system that became even less reliable after an update, even compromising safe driving.

The problem is not a lack of tools. The teams building these major products almost certainly have access to the most advanced AI models. This text is not an anti-AI manifesto. On the contrary, the author believes AI grants developers extraordinary capabilities—allowing small teams to build high-quality, simple, and reliable products that were previously beyond their reach.

Discussion
https://news.ycombinator.com/item?id=49033004
A massive discussion on HN with nearly 700 comments. Users overwhelmingly confirm: software updates now bring anxiety rather than joy. AI has not made mainstream software better—it has simply accelerated the production of mediocre code and fostered irresponsibility ("the AI generated it, not us").

The Test/Reality Gap
https://www.devopsdigest.com/are-ai-coding-tools-hitting-a-ceiling
BlueOptima conducted the BARE (AI Refactoring Evaluation) study, testing 57 models on real-world tasks aimed at improving maintainability (refactoring production code, reducing complexity, and improving structure without changing behavior).

Conclusion: Models are increasingly passing syntactic code checks (> 80%), but they are almost incapable of making changes that actually improve code maintainability. There is also an interesting finding about a plateau: the latest releases of frontier models show almost no improvement on these tasks (stuck in the 17–23% range).

AI is for Prototypes
https://weeraman.com/the-prototype-isnt-the-product/
https://news.ycombinator.com/item?id=49132130
AI is excellent at rapid prototyping, but a prototype ≠ a product. The actual distance from "works on my laptop" to a production-ready system has barely shrunk: architecture, scaling, error handling, security, and maintenance still require the engineering judgment of responsible individuals. Without a deep understanding of the code, an AI-generated project cannot transition from the prototype stage to a stable product.

New model announcements focus precisely on the output code becoming increasingly better and free of bugs—allowing models to generate more code for longer periods while passing all checks. However, does this translate to a better understanding of the actual work of a software engineer?

#agenticcoding #bestpractices