"AI can generate code, but it doesn't grasp efficiency like experienced engineers do. It doesn't prioritize cost-efficient architecture. It doesn't instinctively avoid wasteful service calls, excessive data movement, poor caching, bad concurrency patterns, noisy database behavior, or compute-heavy nonsense that might look good in a code sample but fails in real-world use."
"The applications often work, which makes this approach deceptively effective. The demo succeeds, and, at first, the feature seems to function properly. Everyone congratulates themselves. But then the system is deployed at scale and the cloud bill skyrockets."
Executives are mistakenly reducing software engineering teams, believing AI can independently build and maintain applications. While AI can generate code, it lacks the efficiency and cost-awareness of experienced engineers. Initial successes can mask deeper issues, leading to inflated cloud bills as systems scale. What starts as a manageable cost can escalate dramatically, resulting in millions in unnecessary expenses. The misconception that software engineering is optional can have severe financial repercussions for companies.
Read at InfoWorld
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