AI coding assistants promise speed, but do they deliver? Explore data, developer insights, and security risks showing why AI feels faster but often slows production. Learn where tools like Cursor and Claude Code help, and where they fail.
Is this the same fast to ship but hard to maintain argument we’ve seen a thousand times already?
It’s not a paradox, but a very typical result of using “fast” solutions.
The main paradox here, seems to be: the 70% boilerplate head-start being perceived faster, but the remaining 30% of fixing the AI-introduced mess, negating the marketed time-savings; or even leading to outright counterproductivity. At least in more demanding environments, not cherry picked by the industry, shoveling the tools.
Is this the same fast to ship but hard to maintain argument we’ve seen a thousand times already?
It’s not a paradox, but a very typical result of using “fast” solutions.
The main paradox here, seems to be: the 70% boilerplate head-start being perceived faster, but the remaining 30% of fixing the AI-introduced mess, negating the marketed time-savings; or even leading to outright counterproductivity. At least in more demanding environments, not cherry picked by the industry, shoveling the tools.
I’ll take that as a “Yes”.