Software developers have spent the last two years watching AI coding tools evolve from advanced autocomplete to something that can, in some cases, build entire applications from text prompts. Tools like Anthropic’s Cloud Code and OpenAI’s Codex can now work on software projects for hours at a time, writing code, running tests and, with human supervision, fixing bugs. OpenAI says it now uses Codex to create codecs, and the company recently published technical details of how the tool works under the hood. This has made many people wonder: Is this just AI industry hype, or are things really different this time?
To find out, Ars contacted several professional developers at Bluesky and asked how they feel about these tools in practice, and the responses revealed a workforce that largely agrees the technology works, but is divided on whether it’s entirely good news. This is a small sample size that was self-selected by those who wanted to participate, but their views are still instructive for professionals working in this field.
David Haggerty, a developer who worked on the point-of-sale system, previously told Ars Technica that he was skeptical of the marketing. “All the AI companies are hyping the capabilities so much,” he said. “Don’t get me wrong—LLMs are revolutionary and will have a huge impact, but don’t expect them to write the next great American novel or anything. They don’t work like that.”
Roland Dreyer, a software engineer who has contributed extensively to the Linux kernel in the past, told Ars Technica that he acknowledges the presence of hype but has watched the progress of the AI space closely. “It sounds like incredible hype, but cutting-edge agents are very good right now,” he said. Dreier described a “step-change” in the past six months, particularly after Anthropic released Cloud Opus 4.5. Where he once used AI for autocomplete and the occasional question, he now expects to tell an agent “This test is failing, debug it and fix it for me” and it will do the job. They estimated a 10x speed improvement for complex tasks like Terraform deployment configuration and building a Rust backend service with the Svelte frontend.
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