# Do AI Tools Meet Expectations? Is the promised automation truly effective for software engineers? - **Source:** [Hacker News](https://news.ycombinator.com) - **Published:** 2026-08-09 - **Topic:** [Data & AI](https://brifing.si/c/data-ai) - **Original article:** - **On Brifing:** https://brifing.si/s/c8e0a883-9b67-4f42-80e0-8c61528029cd ## Doubts About AI Tools AI-powered development tools promise a lot to software engineers. However, many of these tools seem far from meeting expectations. Significant issues persist, especially in user experience and system integration. This indicates that the tools are not yet mature. ## The ONA Example: Big Promises, Small Results ONA is a platform that promises to automate software development processes. However, the initial problems users encounter reveal that the platform fails to deliver on its promises. For instance, even login processes are complex and problematic. This shows that the tools are not user-friendly. ## Integration Issues Tools like ONA struggle to integrate with other software tools. For example, connecting with popular project management tools like Linear consumes much more time and resources than expected. These integration issues reduce the efficiency of the tools. ## Cost and Efficiency AI tools introduce additional costs for users. For instance, ONA's 'compute units' deplete quickly, and users do not get the expected results. This shows that the tools are not cost-effective. ## User Experience Problems AI tools complicate users' tasks rather than simplifying them. For example, faulty integrations and incorrect root cause analyses waste users' time. This indicates that the tools are not user-friendly. ## Marketing vs. Reality The marketing strategies of AI tools do not align with their actual performance. Companies selling these tools cannot produce the best software by using their own products. This shows that the tools fail to deliver on their promises. ## Software Engineers' Expectations Software engineers expect AI tools to simplify coding processes. However, these tools complicate processes beyond coding. This falls short of meeting engineers' expectations. ## Future Predictions The future of AI tools depends on resolving user experience and integration issues. Until these problems are solved, widespread adoption seems unlikely. Software engineers await more mature and user-friendly solutions. ## Sources covering this story - https://kirahowe.com/2026/aug/8/is-it-all-just-vapourware — --- _Summary written by Brifing from the original reporting. Copyright in the original article belongs to its publisher; read the full piece at the link above._