Posted / Publication: LinkedIn / SaaS Content Writer | B2B Specialist | SaaS Product | B2B | SEO & Social Media Expert | Book Buff & Storyteller through Book Reviews
Day & Date: Thursday, October 9, 2025
Article Word Count: 308
Article Category: SaaS / AI Development / Tech Commentary
Article Excerpt / Description: A reality check on the current state of AI-assisted coding — why claims like “AI built my whole app” often overlook the human validation still required. This post breaks down why LLMs can’t yet run, debug, or validate code, and why true software engineering still depends on developer context and creativity.
Every week I see developers on Reddit, Inc. claiming, “AI built my whole app,” and similar posts on
LinkedIn touting AI’s coding powers.
Here’s the catch: most of that code still needs human validation. AI has no runtime awareness—it doesn’t see what actually happens when code runs.
✅ Why: Large Language Models (LLMs) generate code based on patterns in their training data, not on actual program execution or feedback.
✅ Even tools like JetBrains AI can’t read npm logs or ESLint output in their own console.
Why: Without real I/O access, AI can’t react to terminal feedback, fix build errors, or validate execution.
✅ Anthropic, the company behind Claude, openly admits:
“AI does not have the ability to run the code it generates—yet.”
✅ Why: Current models can only suggest code, not execute or debug it.
✅ The only time AI code works out of the box is when the pattern already exists in thousands of tutorials and repos.
✅ Why: LLMs excel at repeating known patterns but struggle with untrained, unique scenarios.
✅ React Context + TypeScript? Usually fine.
Something new, creative, or non-standard? Chaos.
Why: LLMs rely on statistical familiarity; innovation breaks their confidence and accuracy.
✅ AI coding feels magical—until you leave the tutorial zone.
Then it’s back to debugging, testing, and real engineering.
Why: Real-world coding demands context, system design, and debugging—things prediction-based AI can’t handle yet.
Curious how other SaaS builders balance AI-assisted coding vs. hands-on development—where’s your line between help and hallucination?
hashtag#SaaS hashtag#AI hashtag#Developers hashtag#ProductMakers hashtag#Coding
FAQ – Why “AI Writes Code” Is Mostly an Illusion
1. Can AI really build a complete app on its own?
Not yet. Most AI-generated code still needs human debugging, validation, and testing before it works in real projects.
2. Why can’t AI run or test the code it writes?
Because LLMs like GPT or Claude don’t have runtime access → they can only predict text, not execute or verify outputs.
3. Does AI coding work better for some tasks?
Yes. It performs well for repetitive or common code patterns found in public repos, but fails on creative or non-standard setups.
4. Will future AI models be able to debug or execute code?
Possibly. Some research aims to give models limited execution access, but it’s still experimental and far from production-ready.
5. What’s the best way to use AI in software development today?
Treat it as an assistant → great for boilerplate, documentation, or quick fixes, but rely on human judgment for system design and debugging.